Computational Design Engineer

CHALMERS TEKNISKA HÖGSKOLA AKTIEBOLAG

Chalmers University of Technology strives for academic excellence while addressing the challenges and needs of society and industry. We offer a creative and dynamic environment for research, education, innovation, and applied science. We are now looking for a Staff Scientist to join our team and help strengthen our ongoing research. About the role As a Staff Scientist, you will become part of a professional research environment where your specialist competence contributes to the quality and continuity of ongoing scientific work. In this role, you are expected to support research activities with your expertise and to work closely with colleagues and research leaders to help maintain and support the development of the research environment. You will be responsible for the digital developments in the Computational Sustainable Design Team, including developing workflows, managing the digital infrastructure and data. The role can be seen as a digital lab manager. You are not expected to build your own research group. About us The Department of Architecture and Civil Engineering tackles contemporary and emerging challenges for the built and natural environment, ranging from resource utilisation, pollution and climate change to digitalisation and inequality. We combine expertise in architecture, civil engineering and related disciplines to build a better world.  The Division Sustainable Built Environments develops the methods, tools, and strategies necessary to transition the built environment toward sustainability. Our work spans from material science to urban planning, bridging the gap between fundamental sustainability science and applied research in collaboration with industry, planners, and citizens. Within the Sustainable Built Environments Research Area, the Computational Sustainable Design Team specialises in developing computational workflows and data-driven design approaches leveraging AI and visualisation techniques to support decision-making processes. Your responsibilities  As a Staff Scientist, you will provide advanced scientific, technical, and methodological support to strengthen research activities. Key responsibilities include: Supporting the Computational Sustainable Design Team with expert knowledge in methods, techniques, and digital developments Support the development and implementation of scientific methods Develop, document and maintain the digital infrastructure Support other team members, e.g. PhD students in data management, coding, and data science Support grant writing Support in supervising PhD and Master thesis students Support teaching to a very limited extend Qualifications and merits To qualify for this position, you should have: A doctoral degree in a relevant field, auch as architectural engineering, urban science, construction informatics, computational design, data science Advanced Coding skills (demonstrable experience in git, python, object oriented programming and building data pipelines) Data management experience in research environments Creating and maintaining data management plans (dmp) Deep knowledge in parametric design, GIS, BIM and urban and building energy assessment, such as Geopandas, postGIS, EnergyPlus, etc. Proficiency in written and spoken English (Swedish language courses are offered.) Excellent academic writing skills The following qualifications are considered meritorious: Peer‑reviewed publications in leading journals Experience in writing research applications Experience in web development Front- and back-end development Teaching and supervision experience  Employment This is a full-time permanent position.   Application     Submit your application in English through our recruitment portal. All files should be in PDF format (max 40 MB each; ZIP files not supported).  Personal letter – your motivation and interest in Chalmers.  CV Certificates and supporting documents – including your doctoral degree certificate, documentation of awards, or teaching merits.  Only complete applications submitted through the portal will be considered.      A background check may be conducted as part of the application process.     Application deadline: July 31st, 2026     Contact     For questions about the position, please contact:     Research area leader, Alexander Hollberg   mailto:hollberg@chalmers.se   Head of Division, Leonardo Rosado   mailto:rosado@chalmers.se       *** Chalmers declines to consider all offers of further announcement publishing or other types of support for the recruiting process in connection with this position. ***        Chalmers University of Technology in Gothenburg conducts research and education in technology and natural sciences at a high international level. The university has 3100 employees and 10,000 students, and offers education in engineering, science, shipping and architecture. With scientific excellence as a basis, Chalmers promotes knowledge and technical solutions for a sustainable world. Through global commitment and entrepreneurship, we foster an innovative spirit, in close collaboration with wider society.   Chalmers was founded in 1829 and still has the same motto today: Avancez – forward.

Ferritico AB

Senior Materials Informatics Engineer Location: Hybrid Company: Ferritico Employment type: Full-time Ferritico is looking for a Senior Materials Informatics Engineer to develop, validate, and productize physics informed machine learning models for advanced materials, with a strong focus on steel, metallurgy, heat treatment, phase transformations, and process-property relationships. This is a mid- to senior-level technical role for someone who enjoys working at the intersection of materials science, computational modeling, machine learning, and product development. About the role You will contribute to the development of computational and machine learning models for metals and metallurgical applications. The role involves translating materials science expertise into clear model logic, validation workflows, technical requirements, and product features. You will help ensure that scientific models are technically sound, well-documented, validated, and aligned with real industrial engineering needs. Key responsibilities Develop computational and ML-based models for steel metallurgy, heat treatment, transformation temperatures, phase transformations, microstructure evolution, and process-property relationships. Define model assumptions, expected behavior, validation datasets, limitations, and acceptance criteria. Improve AI model performance by integrating materials science expertise and physics-informed modeling approaches. Identify data correlations between process, structure, and properties in steel processing and industrial applications. Retrieve, structure, clean, and curate materials data from literature, synthetic datasets, experiments, and other sources. Use Python-based workflows for data treatment, model execution, input preparation, output analysis, and visualization. Test AI software outputs and benchmark results against other simulation or modeling tools. Communicate model behavior, limitations, and results clearly to both technical and non-technical stakeholders. Contribute to documentation, testing, validation reports, and technical decision-making. What we are looking for We are looking for someone with a strong background in materials science, computational materials science, or physical metallurgy. The ideal candidate has: A PhD in Materials Science, Metallurgy, Computational Materials Science, or a related field; or an MSc with relevant experience in materials modeling. Strong understanding of physical metallurgy, preferably including steels, heat treatment, phase transformations, microstructure evolution, and process-property relationships. Experience developing, using, or validating computational models for materials behavior. Familiarity with materials data, alloy compositions, thermal histories, transformation kinetics, or microstructure-property relationships. Ability to use Python for scientific scripting, data treatment, and model prototyping. Strong communication skills and the ability to explain complex materials concepts in a multidisciplinary team. Nice to have Experience with any of the following would be highly valuable: Steel phase-transformation modeling, TTT/CCT diagrams, JMAK kinetics, carbide precipitation. CALPHAD, pycalphad, or similar tools. Alloy design, ICME workflows, ML applied to materials science. Product ownership, technical leadership, or experience translating scientific models into user-facing tools. Data analysis using NumPy, pandas, matplotlib, scikit-learn, Excel-based workflows, or similar tools. This role could be a strong fit if you Recently completed a PhD involving computational or experimental materials modeling. Have an MSc and several years of experience building or using materials models. Enjoy combining materials science with scripting, data analysis, and practical software development. Want to work close to product development rather than only research. Are excited about helping shape digital tools for the future of steel and advanced materials. Why join Ferritico? At Ferritico, you will be part of a Swedish software startup working at the frontier of materials science, AI, and industrial digitalization. You will have the opportunity to influence both the scientific foundation and the product direction of tools used for advanced materials development and manufacturing. We value teamwork, curiosity, technical excellence, and clear communication. Not sure you meet every requirement? We encourage you to apply even if your experience does not match every qualification listed above. We value diverse backgrounds, different perspectives, and people who are motivated to learn and contribute. How to apply Please send your CV and a short note describing your motivation for the role, along with your relevant experience in materials science, metallurgy, computational modeling, or materials informatics, to: contact@ferritico.com (Please include the job title in the email subject line)

SVERIGES LANTBRUKSUNIVERSITET

Department of Ecology The Swedish University of Agricultural Sciences (SLU) is one of Northern Europe largest academic hubs for ecological research and offers a dynamic and excellent research environment with modern infrastructure. The Department of Ecology has about 150 employees, of whom around 40 work at the Grimsö Research Station in Bergslagen. Together, the SLU Ecology Centre and Grimsö Research Station conduct research on sustainable agriculture and forestry, plant protection, nature conservation, and wildlife management, and provide scientific knowledge to inform Sweden and Europe’s environmental policies.   About the position Project Can we foresee the outcomes of public policies, political choices and other decisions? We are finding out by developing serious games and running them using AI. We are building large-scale simulations of societal processes (environmental negotiations, nature conservation policies, and hybrid-threat scenarios) in which every actor is an autonomous AI agent powered by Large Language Models (LLMs). These agents simulate real-world stakeholders, from government ministers to interest groups, and interact through natural language in complex strategic settings. We then run thousands of iterations to map the distribution of outcomes. This effort is part of a new research program Articulating Complexity ( https://www.slu.se/articulating-complexity/ ) hosted at the Swedish University of Agricultural Sciences (SLU) and led by Guillaume Chapron (Docent). It is funded by grants from the Swedish Research Council (VR), the Swedish Foundation for Strategic Environmental Research (Mistra), and the Swedish Research Council for Sustainable Development (FORMAS). Our goal is to build a novel methodology at the intersection of AI, ecology, political science, and complex system analysis that may change how policies are designed and stress-tested and how governments prepare for crises. We are looking for ambitious research engineer to join this effort. Tasks and duties You will be the person who makes the simulations actually run. Concretely, this means: Building the LLM-agent infrastructure (from an existing working implementation). Each agent has a multi-tier memory and an affect state that modifies both prompts and available actions, and a strategic reasoning layer that tracks reputation, commitments, and mental models of other agents. Extending and maintaining the multi-agent simulation framework. A simulation runs dozens of concurrent LLM instances, communicating through structured message-passing protocols. You will develop and maintain this framework, including the validation methodology. Running large-scale Monte Carlo simulations at scale on GPU-enabled HPC clusters (e.g. NAISS). Publishing and dissemination of the project results. You will co-author scientific publications from the project. The research will produce papers at the intersection of computational social science, ecology, security studies, and AI. You will work closely with the PI (Guillaume Chapron https://www.slu.se/en/profilepages/c/guillaume-chapron/) and a postdoctoral researcher in computational social science (to be recruited concurrently). The three of you will form the core team. Your profile Required A degree in computer science, engineering, physics, applied mathematics, or a related quantitative field. A Master or engineering degree is sufficient; a PhD is welcome but not required. Strong programming skills, especially in Python, and sufficient understanding of machine learning/deep learning to work effectively with modern generative AI systems. Ability to assess and incorporate cutting-edge generative AI developments into the project. Experience with software engineering practices such as version control, testing, documentation, modular design, and reproducible workflows. Ability to work independently, debug complex systems under time pressure, and take ownership of technical infrastructure. Desirable Experience with LLMs, including programmatic use through APIs and local deployment. The candidate should be able to select, configure, adapt, and critically evaluate LLMs for research applications. Experience with deep reinforcement learning, agent-based modelling, or multi-agent systems, including the design of simulation environments, interacting agents, decision rules, feedback mechanisms, and scenario-based analyses. Experience with high-performance computing: batch job submission, GPU workflows, and managing large-scale computational experiments. Interest in the application domain: nature conservation, environmental policy, political science, geopolitics and security studies, complex adaptive systems. You do not need a formal background in these fields, but you should find them interesting. Assessment criteria Applications will be assessed on the following: Demonstrated technical ability. We care more about what you have built, than about what courses you have taken. A GitHub portfolio, a deployed system, a well-documented side project, or a track record of solving hard AI problems will carry more weight than a list of credentials. Autonomy and resourcefulness. This is a small team doing frontier work, so the role requires someone who can diagnose problems independently, make pragmatic technical decisions, and keep complex systems running. If that excites rather than worries you, this is the right position. Collaborative disposition. You will work daily with questions from very different disciplines: ecology, political science, geopolitics. The ability to communicate technical constraints and possibilities to non-technical collaborators is essential. Interpersonal skills will form an important part of the assessment. Location: The position is based either at Grimsö or at Uppsala, with a possibility to be partly based at another academic Swedish institution with a core expertise in AI and machine learning. Form of employment:  Fixed-term employment 12 months with the possibility of prolongation.  Scope: 100%. Start date: As agreed, as soon as possible after recruitment.  Application: Please submit your application before deadline 14 July 2026. You can submit your application by clicking the button below. This is a shortened advertisement. Please refer to the full advertisement on the SLU website for complete information about the position and details on what to include with your application.