Three PhD Positions in foundations and algorithm design for Machine Learning, SURE-AI

Up to 3 PhD Research Fellowships are available at Simula Metropolitan Center for Digital Engineering (SimulaMet) for highly-motivated and self-driven PhD students with strong intellectual curiosity and interest in research.

These positions are part of the National Norwegian Centre for Sustainable, Risk-averse and Ethical AI (SURE-AI), funded by the Research Council of Norway (RCN) (2025-2030), coordinated by Simula Research Laboratory (Simula). Simula is a leading Norwegian research institute known for its excellence in cutting-edge ICT with a strong track record of top evaluations, active international collaborations, and successful in significant funding initiatives, including European and National RCN grants. SimulaMet is a research centre jointly owned by Simula and Oslo Metropolitan University. It is the home of Simula’s research activities on networks and communications, artificial intelligence and IT management, and it is OsloMet’s strategic partner in digital engineering. SimulaMet supports talent development through PhD and postdoctoral programs and provides valuable research infrastructure, such as the national HPC facility, eX3, and network testbeds.

About SURE-AI

The primary objective of SURE-AI is to create a new generation of algorithms for inference and decision-making by pushing the boundaries of the underlying computational techniques. SURE-AI researchers will drive a transformative leap in AI through a groundbreaking Centre initiative dedicated to making AI more efficient, transparent and safe, addressing the critical challenges of resource consumption, behavior unpredictability and ethical alignment with human and societal values. The centre involves senior researchers from well-renowned national and international research institutions, in close collaboration with industry and public sector. Overall, SURE-AI includes 19 national Norwegian partners, as well as researchers from 15 international institutions. 

SURE-AI is firmly committed to excellence in both cutting-edge research and training of outstanding young researchers, as described here. Applicants are expected to actively contribute to one or more of SURE-AI’s work packages and innovation clusters, engage in collaboration across the SURE-AI network, and participate in all relevant centre activities. 

Job Description

We are looking for PhD students who will be part of an interdisciplinary research environment including also international collaborators. Each PhD student is expected to cover the three fronts of theoretical analysis (e.g. performance guarantees), algorithm design and implementation, carrying out research on one or (preferably) two of the following interconnected topics:

• Topic 1: Learning explainable representations capturing optimally the complex spatio-temporal dependencies and geometry (e.g. higher-order, causal dependencies, dynamics in latent spaces, etc.) in multivariate and possibly irregular data, as well as exploiting those learned representations for various inference, reasoning, decision-making and control tasks, considering also robustness and reliability against noise, outliers, data scarcity or missing data. These representations will also allow integrating structural inductive biases or different types of prior knowledge (e.g. domain knowledge, physical laws, ontologies), as well as quantifying and tracking uncertainties, without necessarily distributional or asymptotic assumptions. Both batch and online algorithms will be considered, understanding how to design the best optimization-based training algorithms. This position/topic will be within the SIGIPRO Department at SimulaMet, with possible collaborations across other Departments at Simula, as well as other national and international partners in SURE-AI. 
• Topic 2: Constrained machine learning algorithms for training, inference and control, where constraints may come from different perspectives, such as safety, risk-aware constraints, physical systems principles, ethical constraints, fairness, privacy, regulations, or constraints in terms of some of the rewards for instance in the context of reinforcement learning. This area is relevant for many problems, including adapting and fine-tuning foundation models under different constraints or human alignment requirements, or incorporating various risk-aware measures in decision-making or control problems (e.g. reinforcement learning), or ethically embedded machine learning algorithms. This position/topic will be within the SIGIPRO Department at SimulaMet, with possible collaborations across other Departments at Simula, as well as other national and international partners in SURE-AI. 
• Topic 3: Efficient methods and computational architectures for scalable, resource and data efficient, and domain-adaptable machine learning, considering both centralized and decentralized (multi-agent inter-connected systems) machine learning scenarios. This includes co-design of training and inference algorithms jointly with advanced high-performance disaggregated computation architectures, as well as alternative disruptive architectures, such as (most importantly) neuromorphic computing or (possibly) quantum computing. This may involve algorithmic reformulations that increase arithmetic intensity, data locality, memory efficient attention mechanisms and joint computation-communication efficient strategies, surpassing the “memory wall” bottleneck and reducing energy demands. In the context of large models, such as foundation models (e.g. LLMs), this may involve techniques such as low-rank and sparsity approximations, tensor decomposition methods, model pruning, quantization, compression, splitting methods, stochastic and dynamical systems approximations (e.g. neural ODEs/SDEs, neural operators), and approximate mixed-precision training and inference strategies. This is a multidisciplinary topic and will include collaboration across several departments in Simula, including HOST, SCAN, as well as an additional postdoctoral researcher from SIGIPRO through the RCN Program “Recruitment of Talented Researchers to Norway” for National Centres.
• Topic 4: Generalizable, continual and online learning methods designed to operate under strict computational, data, and reliability constraints. By shifting from offline, centralized training to continual and online learning, this topic addresses time-varying data distributions, adaptation across tasks or environments, and non-IID data scenarios where traditional benchmarks and strategies typically fail. Generalization in inference and control problems can be increased by leveraging structural dependence representation learning and counterfactual reasoning to enable out-of-distribution generalization and active planning, ensuring robustness against noise and unseen samples. Automated hyperparameter optimization is also relevant to adapt to non-stationary distribution shifts as well as plasticity to adapt reliably to new tasks and environments, and maintain stability. In addition to the design of algorithms, it is important also to characterize the generalization capability in terms of providing guarantees, generalization error bounds, or in cases of transfer learning across tasks, characterizing the propagation of uncertainties. This position/topic will be within the SIGIPRO Department at SimulaMet, with possible collaborations across other Departments at Simula, as well as other national and international partners in SURE-AI. 

All these projects/topics will incorporate real data in the context of impactful applications within the verticals of energy and environment, finance and societal systems, in cooperation with some of the public or private partners involved and postdoctoral/senior researchers in the SURE-AI Center.

The position is available for a period of 3 years. Given the availability of additional funding and a good match with the SimulaMet needs, the position may be extended to a maximum of 4 years, including other duties and/or research stays to some of the national or international partners in SURE-AI.

Selection criteria 

We will consider candidates who have a BS and MS degree (both) in Electrical Engineering, Computer Science or Applied Mathematics. data science/machine learning, signal processing, and applied mathematics. Other required qualifications include:

• A strong background in at least two (or preferably more) of the following topics: Linear algebra, optimization, data science, machine learning and signal processing, proved by top grades.
• Previous experience with the challenges of real data or advanced computation architectures for machine learning are a plus, but not required.
• Fluency in Python and/or MATLAB with a well-established background in programming, proved by relevant programming courses.
• An average GPA of at least B in BS, and A in MS.

Interest in and ability to collaborate across disciplines and institutions.Relevance of profile to SURE-AI’s aims and objectives.

• Sharing Simula’s core values: excellence, impact, curiosity, ambition and respect.

All candidates will also have to demonstrate an excellent level of spoken and written English, possess good interpersonal and communication skills and show willingness to work as part of an international team.

Simula offers

• Excellent opportunities for performing high-quality research, as part of a highly competent and motivated team of international researchers and engineers.
• An informal and inclusive international working environment.
• Generous support for travel and opportunities to build international networks, through established collaboration with industry, exchange programs and research visits with other universities, and funding to attend conferences.
• Modern office facilities located in downtown Oslo.
• A competitive salary. Starting salary from NOK 550 800.
• Numerous benefits: access to a company cabin, sponsored social events, generous equipment budgets (e.g., computer, phone and subscription), subsidized canteen meals, comprehensive travel/group life insurance policy, etc.
• Relocation assistance: accommodation, visas, complimentary Norwegian language courses, etc.
• Administrative research support: e.g., quality assurance process for grant proposals (including RCN and EU programs).
• Wellness and work-life balance. Our employees’ health and well-being are a priority, and we encourage them to make use of our flexible work arrangements to help balance their work and home lives efficiently.

Application

Interested applicants are requested to submit the following:

• Curriculum vitae showing your educational background, working experience (in particular, any relevant academic or industrial work), a list of scientific publications and talks.
• Transcripts for both Bachelor and MS degrees
• Cover letter outlining your motivation for applying, relevant experience and qualifications, research interests and how/why you are qualified for the position. 
• Contact information of two references.

Please note that all documents must be in English language. 

Application with attachments must be submitted via our recruitment system, click "Go to application form". Applications submitted by email will not be considered.

Do not submit other certificates, articles, and the like unless specifically requested. If further documentation is wanted for assessment, you will be asked to submit it later. 

Deadline: April 6, 2026

Short-listed candidates will be invited for an interview. Applications will be screened continuously and a decision will be made as soon as we find the right candidate. Flexible starting date.

General Information

Six national research centres for Artificial Intelligence (AI) launched in the Fall of 2025. Each of these centers will foster interdisciplinary collaboration among researchers to address both scientific and societal challenges, develop new technologies, understand the impact of AI, and drive innovation across both the business and public sectors. This initiative is established and financed through the government's significant investment in artificial intelligence over the next five years.  SURE-AI is one of these six centres.

About SimulaMet, Simula Research Laboratory and Norway

Simula is organised as a group of legal units which together perform Simula's activities in research, education and innovation. The name “Simula” often refers to this group of companies. SimulaMet is part of the Simula group, and performs high quality research in the fields of machine learning and data science, IT management and communication systems. 

Simula Research Laboratory AS is a publicly owned research lab. Simula conducts Information and Communication Technology (ICT) research in the fields of scientific computing, machine learning, software engineering, communication systems, and cybersecurity. SimulaMet and Simula Research Laboratory are both located centrally in Oslo, Norway.

Simula's main objective is to create knowledge about fundamental scientific challenges that are of genuine value for society. This is achieved through high quality research, education of graduate students, industry collaboration, technology transfer, and commercialization. Since 2001, scientific evaluations conducted by the Research Council of Norway have repeatedly placed Simula at the forefront of international research in ICT. The most recent in-depth evaluation was published in 2025, grading our full range of research as excellent, ranking SIMULA the highest of all Norwegian research institutions regarding impact and publication quality (p14. in National EVALMIT report). 

Simula appreciates diversity. We currently employ approximately 200 individuals from 35 countries and strive to create a family-friendly working environment. We are an equal opportunity employer and encourage women to apply.                                   

Simula has a close collaboration with leading universities in Norway and abroad, both in terms of research and education, and facilitates extended research stays abroad. Our Master's and PhD students conduct their research at Simula and attend courses and receive their degrees from university partners.                                         

Norway is famous for its epic outdoor scenery, with wonderful fjords, gorgeous waterfalls and majestic mountain ranges. Moreover, Norway is a safe and peaceful country with a thriving economy, offering a high standard of living and all the benefits of a social-democratic welfare state, such as social security and universal public healthcare.

Learn more about Norway: Official travel guide to Norway

Simula: solving problems, making a difference.

Simula’s mission is to solve important and fundamental problems of science and engineering, with the main goal of improving society. We do this by concentrating on five select research areas in ICT and performing excellent research, by developing our people, and by spinning out applicable results into profitable startups. 

Learn more about: working at Simula and careers at Simula

Simula uses Semac´s background check in our recruitment process.

According to the Norwegian Freedom and Information Act (Offentleglova) information about the applicant may be included in the public applicant list, also in cases where the applicant has requested non-disclosure.

Employment for this position will be made according to the Security Act and the Act relating to Control of the Export of Strategic Goods, Services and Technology. Candidates who by assessment of the application and any attachments are considered to be in conflict with these regulations will not qualify for this position.

Questions about the position, please contact:

Baltasar Beferull-Lozano

Professor and Chief Research Scientist

Director of National SURE-AI Centre

Head of SIGIPRO Department

Simula Metropolitan