Jobs

On-going positions, various deadlines

We are looking for a motivated, ambitious PhD student to join the Cryptography Group in the Cybersecurity Engineering Section at DTU Compute (Copenhagen region, Denmark).

This 3-year PhD position (starting on 1 September 2026) will focus on secure Multi-Party Computation and Zero-Knowledge Proofs.

You’ll join a growing, collaborative team of cryptographers at DTU, and you’ll get the chance to connect and work with leading researchers in the Copenhagen region and abroad. 

The focus of the PhD project is on advancing the theory and practice of secure Multi-Party Computation (MPC) and Zero-Knowledge (ZK) Proofs. The successful candidate will investigate the formal design and security analysis of MPC protocols and ZK proofs, with a particular emphasis on foundational aspects. The project will also explore efficiency and post-quantum security, aiming to bridge theoretical advances with their relevance to practical applications.

The Department of Mathematics and Computer Science (IMADA) at the University of Southern Denmark (SDU) invites applicants for several positions as Assistant Professor in University Science Education Research, with a particular focus on learning environments, experience and design. All positions contribute to research on how learning environments and experience shape student engagement, learning and study trajectories in STEM education. We invite applications from candidates with disciplinary backgrounds in Mathematics, Computer Science, Biology, Physics, Chemistry, Pharmacy or related fields, as well as Education, Pedagogy, Psychology, Design or Learning Sciences, who wish to develop a strong research profile in STEM education. 

Applicants are not expected to cover all areas described in this posting; depth in one profile area is valued. 

SDU Center for Industrial Software at the Maersk Mc-Kinney Moller Institute, University of Southern Denmark (SDU) is seeking a highly motivated postdoctoral researcher for a two-year position within the VANTAGE project (Vulnerability Assessment aNd Testing Automation for Global Enhancement). 

The VANTAGE project leverages a multi-agent AI architecture with advanced reasoning capabilities (chain-of-thought reasoning) to transform security operations. It integrates autonomous vulnerability assessment and penetration testing (VAPT), AI-based orchestration, and advanced forensic analysis to enhance cybersecurity operations for modern Security Operations Centers (SOCs) and Computer Security Incident Response Teams (CSIRTs).

We are looking for applicants with strong background in the design and development of multi-agent AI systems in the context of cybersecurity operations and also demonstrated ability to conduct high-quality research. The position is funded under the EU DIGITAL-ECCC programme focusing on the deployment of advanced cybersecurity technologies. It involves leading European research institutions, universities, SMEs, and cybersecurity organizations. The successful candidate will join an interdisciplinary research environment and work under the supervision of Professor Sadok Ben Yahia and Assistant Professor Sani Abdullahi while closely collaborating with academic and industrial partners across Europe.

The position is available from May 2026, or as soon as possible thereafter.

The postdoc will develop machine learning for protein engineering, including: (1) investigating how epistatic effects in proteins can be captured by machine learning models; (2) develop machine learning models for protein engineering that are able to optimize additive as well as epistatic effects.
The work will be in collaboration with domain experts who will be working on optimizing an NADPH-dependent enzyme called formate dehydrogenase. Optimizing this enzyme will be a model system used to evaluate the developed methods.
You will join a highly motivated and collaborative team under the supervision of Søren Hauberg, Wouter Boomsma, and Carlos G. Acevedo-Rocha. The research environment favors creative ‘slow thinking’ style research and we emphasize having fun along the way. Our work is consistently published at the top venues of machine learning and protein research.

At the Faculty of Engineering and Science, AAU Energy, a position as Postdoc in AI-augmented process control, is open for appointment from 01 July 2026 or soon hereafter. The duration of the position is two years.

The main task of this postdoc position will dedicate on research and development of advanced process control augmented by some AI techniques, e.g., using reinforcement learning, for offshore industrial produced water treatment processes. The developed methods/solutions should be tested and demonstrated on a globally leading pilot-plant sited at Aalborg University. There are also potentials to be tested at relevant industrial partners’ installations at the late stage of the project. This position is fully supported by a new EUDP project named ARROiW – Advanced Recording for Reduction of Oil-in-Water, which aims to develop advanced online oil-in-water sensing and control technologies applicable for offshore industrial processes. The ARROiW project involves academic partners as Aalborg University, Technical University of Denmark and Copenhagen University, as well as relevant industrial partners.

We are searching for a highly motivated PhD candidate to conduct research on the design, development, integration, and evaluation of a multimodal, Socratic AI tutor for novice programmers within Aalborg University’s problem-based learning (PBL) model.

The position is anchored in the HumanMachineInteraction research environment at Department of Architecture, Design and Media Technology at Aalborg University (Aalborg CREATE city campus) on the PhD program Media, Architecture & Design, and forms a central part of the externally funded BuddhAi project, which is an interdisciplinary AAU initiative that aims to redesign how students learn programming through AI-driven, dialogue based, and pedagogically grounded tools.

The PhD candidate will contribute to a cross-faculty collaboration spanning the TECH and SUND faculties, working closely with course teams from Medialogy and Clinical Science & Technology, as well as a dedicated pedagogical postdoc.

The stipend is open for appointment from 1 August 2026 or soon hereafter. The duration of the position is three years.

The Department of Department of Dentistry and Oral Health at Faculty of Health at Aarhus University invites applications for a position as Postdoc in the field of in the field of advanced image processing as per 1 July 2026 or as soon as possible thereafter. The position is a fixed-term full-time position for 1 year.

Our group is part of a larger initiative to implement state-of-the-art data science principles into dental practice. While our primary focus is on the use of deep learning in (dental) imaging, our work expands into any type of data (e.g. tabular data) as well as various other algorithmic methods for data processing and analysis. 

Current projects within this scope include, but are not limited to:

• Detection and classification of lesions
• Segmentation of anatomical and pathological regions of interest
• Image enhancement e.g. improving resolution, noise, artefacts etc.
• Synthetic radiography

Join a young research group dedicated to transforming materials discovery from empirical exploration to predictive design. Our research program builds on a synergy of high-throughput virtual screening, generative machine learning (ML) models, and automated reaction exploration to gain insights into complex chemical systems.

As a PhD candidate, you will work on developing synergistic workflows to model dynamic, multi-phase systems. Your primary focus will be to overcome the limitations of current simulation methods by enhancing machine learning interatomic potentials and develop ML approaches to model rare events and complex environments. A key focus will be solving the "chicken-and-egg" problem of sampling: developing algorithms that learn to bias simulations automatically without requiring prior knowledge of the mechanism. You will contribute to open-source software frameworks and develop strategies to guide open-ended explorations systematically toward desired targets. The project involves strong collaborations with domain experts to align your theoretical models with large-scale experimental datasets.

NQCP invites applicants for PhD fellowships in the Algorithms & Applications (A&A) section within this area.

Start date is 1 October 2026 or as soon as possible thereafter during 2026.

We are interested in developing a project plan with prospective candidates to suit their skills and experience. Current priority areas for A&A are:

• Model Hamiltonians for chemical systems
• State preparation techniques
• Schemes for extracting information and observables from quantum computation
• Noise modelling of logical quantum circuits

A project can span one or multiple of these areas.

We are seeking highly motivated and talented applicants for a 1-year postdoctoral position (with the possibility of 1-2 year extension) in the area of efficient foundation model inference.

This is a full-time, fixed-term position for one year, starting on September 1, 2026, or as soon as possible thereafter.
The initial appointment will run until August 31, 2027, or for 12 months from the actual start date. An extension of 1–2 additional years may be offered, subject to satisfactory performance.

The successful candidate will contribute to cutting-edge research on topics including:

• Token compression and adaptive token pruning;
• Distributed and collaborative inference strategies;
• Mixture-of-Experts (MoE) architectures for scalable inference;
• Resource-aware and latency-constrained inference optimization;
• Edge intelligence and on-device foundation model deployment.

Applications are invited for a PhD fellowship/scholarship at Graduate School of Natural Sciences, Aarhus University, Denmark, within the Computer Science programme. The position is available from August 2026 or later. 

Digital platforms have transformed how we collaborate, but they consistently fail when physical objects are central to the work. Whether evaluating fabric in clothing design, assembling machinery components, or co-creating with physical prototypes, remote collaborators lose the tactile, spatial, and multisensory qualities that make these interactions meaningful. 

The MIRA project (ERC Consolidator) addresses this gap. The project develops a new theoretical framework and a novel class of interactive tools called multimodal proxies that integrate visual, auditory, and haptic feedback to support richer, more effective remote interaction with physical objects. 

The PhD student will work on WP1: Developing Foundational Principles. 

Applications are invited for a PhD fellowship/scholarship at Graduate School of Natural Sciences, Aarhus University, Denmark, within the Computer Science programme. The position is available from August 2026 or later. 

Modern software systems — from operating systems and distributed databases to concurrent applications and security-critical infrastructure — are notoriously difficult to reason about correctly. The goal of formal program verification is to provide rigorous mathematical guarantees about the behavior of such systems, going beyond testing to establish correctness by proof. This PhD project is situated at the foundational end of this endeavor: rather than applying existing verification tools, the aim is to develop the logical and mathematical foundations that make principled verification possible.

The research will focus on separation logic and its application to the verification of concurrent and distributed programs. Separation logic is a powerful program logic that supports modular, compositional reasoning about programs that manipulate shared state. The work is mathematical in nature and combines ideas from logic, type theory, and programming language semantics. Recent advances — exemplified by the works building on the Iris framework, which is developed in part at Aarhus University — have extended separation logic to handle higher-order programs, fine-grained concurrency, and distributed computation, e.g., the Trillium and Aneris program logic frameworks. The PhD project will contribute to this line of work, with possible directions including the design of new program logics, the development of semantic models, and the mechanized verification of programs in the Rocq proof assistant.

Do you have a passion for solid mechanics and software, and do you want to create solutions for the world's green transition? You will be part of a research group which takes a holistic approach to wind turbine blades, integrating all aspects that contribute to their structural integrity, performance, and sustainability.

We seek a Postdoc who can contribute to the development of computationally efficient modelling, data integration, and digital twin technologies within our projects, with a strong emphasis on real‑time prediction and interaction. One of our main projects are ReliaBlade-2 with more info at https://reliablade.dtu.dk/. You will join a cross-disciplinary, international team collaborating on supporting the R&D needs for key industry players in the wind energy industry. 

At the Technical Faculty of IT and Design, Department of Computer Science, one or more PhD stipends/Integrated PhD stipends in representation, compression, learning, and inference for classical and quantum data are available within the general study programme in Computer Science. 
The stipends are open for appointment from August 1, 2026, or soon as possible thereafter.

The stipends are available for 3 or 4 years depending on whether the applicant already holds a master’s degree. Applicants who hold a master’s degree can apply for a 3-year position, and applicants who have finished 4 years of full-time studies but do not hold a Master’s degree can apply for a 4 years integrated PhD stipend. The Integrated PhD stipends are only open for appointment with starting date 1 September 2026. 

Are you ready to launch your scientific career by addressing the urgent need of renewable energy integration? At DTU Construct you will break new ground at the absolute forefront of what is possible in the field of energy storage. 

We invite applications for a fully funded PhD position within the fields of Thermal Energy Storage (TES) and Artificial Intelligence at the Department of Civil and Mechanical Engineering, Technical University of Denmark (Thermal Energy Section).

We seek a talented, self-motivated, and collaborative researcher to develop physics-informed AI methods and transform decision-making in the design and operation of next-generation thermal energy storage systems, including latent heat TES and thermochemical TES. 

Your work will advance a paradigm shift in TES system design, modelling, and control by developing adaptive, physics-informed models integrating thermo-fluid dynamics, deep learning neural networks, and experimental data. A key objective is to overcome limitations of traditional thermo-fluid dynamic approaches in design discovery and TES performance evaluation.

The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing Assistance in Complex Acoustic Environments within the general study programme Electrical and Electronic Engineering. The PhD Stipends are open from August 1, 2026, and the integrated PhD stipends are open for appointments from September 1, 2026.

The scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation models to design adaptive, efficient, and intelligent algorithms for hearing assistive devices. Key objectives include improving speech perception in noisy and unpredictable environments, reducing listening effort, and developing systems capable of identifying and prioritizing the user’s intended sound sources. 

Overall, the PhD works contribute to the development of intelligent, user-aware hearing assistance systems that aim to match or exceed normal-hearing performance in real-world listening scenarios. Practical applications of the proposed research include hearing assistance technologies for individuals with hearing impairments such as advanced hearing aid systems but also - more broadly - solutions to enhance speech communication (hearing impaired or otherwise) in acoustically challenging situations e.g., headsets, earbuds, telepresence solutions, etc. 

The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Foundation Models for Neural Speech Tracking within the general study programme Electrical and Electronic Engineering. The PhD Stipends are open from August 1, 2026, or soon hereafter, and the integrated PhD stipends are open for appointments from September 1, 2026.

The PhD project falls under Research Track 2: Tracking neural and perceptual attributes.
The research investigates how the brain learns and tracks acoustic patterns over time. It focusses on accurately capturing neural encoding of perceptual statistical properties and uncovering the mechanisms driving auditory recognition. The methods will range from context tree models to large language models that generate embeddings capturing semantic and statistical relationships.

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 15 August 2026 or later. 

This doctoral project will investigate exciting new ways of digitalizing district cooling systems in megacity environments with hot and humid climates through Digital Twins (DT), including system design and installation support, optimized control, system monitoring, and fault detection, with a major emphasis on scientifically sound modelling and prediction, together with rapid replicability so that solutions can be quickly deployed in new districts.

This raises fascinating research challenges that will be addressed as the core activities of the PhD position:

• Investigate information modelling requirements for effective Digital Twinning of district cooling systems that spans from building to district scales i.e. Building Information Modelling (BIM), Geographic Information System (GIS) modelling standards
• Develop effective algorithms that extract and align the right semantic, geometric, and topological information from diverse models and data sources to be used in cooling system Digital Twins
• Develop evidence-based methods for system design and installation support, e.g. injecting configuration faults into BIM models to predict problems with real-world installations
• Towards rapid replicability, exploit and extend the Digital Twins as a Service (DTaaS) platform for creating and deploying cooling system digital twins by creating reusable DT assets tailored to BIM and GIS
• Develop services for online monitoring and prediction of expected demand and anomaly properties, utilising the FMI-supported Maestro framework for co-simulations between adequate digital twin models (DTaaS), and (initially) the statistical model checking engine of UPPAA
• Through DTaaS, develop new methods that orchestrate AI-driven analysis tools and simulators for multi-scale cooling system prediction, optimized control, monitoring and fault detection, in collaboration with AAU and Grundfos

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Electrical and Computer Engineering programme. The position is available from 01 August 2026 or later. 

Key Research Objectives. The objective of this PhD is to develop a high-performance, low-latency framework for Test-Time Adaptation (TTA). This involves designing autonomous architectures capable of monitoring and maintaining the reliability of unimodal and multimodal foundation models in real-time. Key research pillars include:

• Autonomous Monitoring: Developing mechanisms to detect distribution shifts and quantify model uncertainty across heterogeneous data types.
• On-the-Fly Adaptation: Designing lightweight TTA algorithms that can recalibrate models at the edge under strict latency and computational constraints.
• Efficiency and Reliability: Balancing the trade-offs between adaptation accuracy, energy efficiency, and hard real-time execution.

The successful candidate will join a pioneering research group focusing on the next generation of adaptive AI, with the opportunity to publish at top-tier machine learning and computer vision venues (e.g., NeurIPS, ICML, CVPR) and validate research on state-of-the-art edge computing testbeds.

Be part of the future of creativity support for professionals at Aalborg University!

This interdisciplinary project investigates how generative AI can be incorporated into the building design and operation process. Particularly, we seek to support architects to design more sustainable buildings through simulation-assisted performance feedback regarding initial design concepts. We will explore how AI systems can augment professionals’ creativity toward concurrent optimization of human health and energy efficiency. This PhD position will combine fieldwork in real-world architectural settings with human-centric, environmentally responsible application of AI tools.

You will join an active, interdisciplinary research team with strong ties to architectural practice.

The position is open for appointment as of August 1st, 2026, or soon hereafter. The position is a three-year role, located in the Department of Computer Science at Aalborg University's Technical Faculty for IT and Design. In addition to the Department of Computer Science, the project involves close collaboration with the Department of Architecture, Design, and Media Technology and is part of a larger project involving at least one other PhD candidate and a postdoctoral researcher. The candidate’s workplace will be in Aalborg.

The PhD candidate will study the needs of creative professionals, in particular architects, in integrating decision support during design ideation. Your work will combine requirements gathering with interactive system development and empirical evaluation in real world settings. You will have the opportunity to shape the project based on your interests and in collaboration with a leading architectural firm.

The position is open for appointment as of August 1st, 2026, or soon hereafter. The position is a three-year role, located in the Department of Computer Science at Aalborg University's Technical Faculty for IT and Design. In addition to the Department of Computer Science, the project involves close collaboration with the Department of Architecture, Design, and Media Technology and is part of a larger project involving at least two PhD candidates. The candidate’s workplace will be in Aalborg.

The postdoctoral candidate will develop and evaluate interactive generative AI prototypes that serve as creativity support tools for architectural design practice. Findings will inform the design and evaluation of generative AI systems and their ability to provide interpretable feedback on e.g. energy performance for conceptual design. You will have the opportunity to shape the project based on your interests and in collaboration with a leading architectural firm.

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 1 October 2026 or later.

Understanding when genes are functionally redundant or influence multiple traits (pleiotropy) is important for accelerating modern plant breeding. This project aims to develop computational tools, including deep learning and AI, to prioritise candidate genes by integrating evolutionary and functional information.

The selected candidate will work on three main objectives:

1. Develop an algorithm that integrates evolution and functional information using comparative genomics and deep learning approaches.

2. Apply this framework across the plant kingdom to identify conserved patterns of gene redundancy and pleiotropy.

3. Identify genes associated with key agronomic traits, such as flowering time and root architecture.

This project will provide the PhD student with the opportunity to develop skills in comparative genomics, phylogenomics, and deep learning, while contributing to computational approaches that support crop improvement.

Applicants are invited for a PhD fellowship/scholarship at Graduate School of Technical Sciences, Aarhus University, Denmark, within the Quantitative Genetics and Genomics programme. The position is available from 1 September 2026 or later. You can submit your application via the link under 'how to apply'.

Gene duplication is a major driver of evolutionary innovation in plants and has played an important role in the emergence of traits relevant to crop domestication and improvement, such as flower morphology and stress responses. This PhD project aims to investigate how duplicated genes diverge or retain functions, and how these processes contribute to the evolution of agronomically important traits in domesticated plants. By combining comparative genomics, evolutionary analyses, and computational approaches across multiple crop species, the project will help identify candidate genes relevant for crop research and future breeding strategies. The results will also provide new insights into the evolutionary mechanisms shaping plant genomes.

We are seeking applicants for an 18-months postdoctoral fellowship focusing on the development of an AI-based phenotyping system for high-throughput screening for aphid performances on pea plants. The position is available from the 1st of November 2026 or as soon as possible thereafter.

The aim of the project is to develop an AI-based phenotyping system for high-throughput screening of aphid resistance in plants both in controlled and field conditions. This tool will be used to screen for aphid resistance in a pea germplasm collection by characterizing aphid-traits related to their performances. In the long term, this will contribute to selection for aphid-resistant pea varieties contributing to sustainable organic agriculture in Denmark.

The appointed candidate will work in a highly experienced research environment, spanning basic biology to applied research for industry outcomes. The hosting team is a multidisciplinary group with expertise in applying various quantitative and statistical modelling approaches to biological systems (including crop genetics, host-pathogen interactions, pathogen population genetics, evolutionary biology...). The candidate will work in close collaboration with a specific group (DARSA) specialized in developing and applying remote-sensing tools and innovative open-source machine-learning methods.

You have a background in Graph Theory, strong mathematical competences and would like to advance your career by conducting research in this field and earning a PhD?

The PhD project is in Graph Theory. It focuses on basic mathematical research concerning Structural Digraph Theory and the intersection of Graph Theory and Combinatorial Optimisation.

Your main tasks will consist of conducting research regarding relaxations of Woodall’s Conjecture about directed cuts and dijoins, and further studies regarding properties of related objects and certain classes of digraphs. Additionally, you will be responsible for supporting the research project by computer aided tasks when reasonable and applicable. You will work with nearby colleagues, and with partners in Denmark as well as abroad. You will also be involved in teaching assistant duties at DTU Compute.

Do you want to be involved in the green energy transition? Do you wish to contribute to the development of machine learning methods with real-world impacts on power electronic converters and drives? Are you looking for a career in R&D or academia? We have multiple opportunities for you!

We invite applications for multiple 3-year fully funded PhD positions that will contribute to the development of a new generation of embedded intelligence within grid-connected power converters and variable-frequency motor drives with edge computing and machine learning capabilities.

We offer a multidisciplinary, international, and friendly atmosphere, encouraging creativity, diversity, empathy, and teamwork. You will have a great opportunity to build strong networks with internationally known researchers at DTU and other universities as well as industrial partners.