• Development of theoretical models.
• Implementation of these models in a finite element code.
• Study of different dynamical regimes depending on model parameters.

The postdoctor position is part of a new national research project "Human-Centered Intelligent Realities (HINTS)" carried out with six industrial partners and is partly externally funded by the KK Foundation. In co-production with our corporate partners and the community, we develop concepts, principles, methods, algorithms, and tools for human-centered intelligent realities, to lead the way for future immersive, user-aware, and intelligent interactive digital environments. The project is divided into five separate Research Themes (A-E) which collaborate within the framework of sub-projects. The postdoctor position in this call will primarily be involved in Research Theme B/C.

Research themes in HINTS:

• Research Theme A: Novel Experience Assessment Methodologies for Intelligent Realities
• Research Theme B: Novel Environment and Interaction Techniques for Intelligent Realities
• Research Theme C: Visual Analytics for Intelligent Realities
• Research Theme D: Adaptive and Distributed AI for Intelligent Realities
• Research Theme E: Networking for Intelligent Realities

The focus of Theme B/C is in the area of intelligent realities with the development of new intelligent virtual 3D environments, interaction techniques, and immersive visual analysis/visualization. The challenge lies in individualizing the design, management, and use of these environments, adapting the interaction techniques, and providing visual analytics to improve the overall user experience. Artificial intelligence (AI) will play a crucial role in the individualization process. The growing availability of user data helps designers identify user trends and can be leveraged to shape user environments and visual analytics. A postdoctor will essentially carry out research. The position may involve teaching up to a maximum of one-fifth of the working hours.

• Determine and quantify phytoparasitic nematodes of soil and plant material.
• Study the plant-nematode interaction and multitrophic relationships.
• Analyze the diversity and microbial activity of the soil.
• Carry out gene expression dynamics related to plant defense mechanisms.
• Design the in vitro test experiments.
• Crop management.
• Process data and make reports.

• Collecter les prix OTC afin d’enrichir la base de données unique de l’entreprise FIT SA.
• Développer une plateforme de simulation associée à un générateur de scénario reposant sur ce nouveau modèle de la courbe à terme afin de déterminer l’exposition au risque d’un ensemble de positions données.
• Proposer une méthode statistique permettant de tester la validité des hypothèses statistiques retenues.
• Développer une stratégie de couverture afin de piloter l’exposition au risque du groupe FIT SA.

• Develop biofunctionalized hydrogels with cell loading capabilities.
• Carry out functionalized hygrogels with specific rheological and mechanical properties that allow cellular incorporation.
• Injecting hydrogel into a biodegradable scaffold for bone tissue engineering.

Name and description of the research project:

The role of agency and morality in dehumanization - an integrative approach

In this project, we undertake to study the predictors of dehumanization (Aim 1) and interventions to reduce dehumanization (Aim 2). We propose a systematic research program in cooperation with partners from Israel, Spain and Great Britain. To explore the causes of the dehumanization of others (Aim 1), we will examine whether the two most basic dimensions by which we judge other people, i.e. agency and morality, contribute to dehumanization. To explore interventions that reduce dehumanization (Aim 2), we will focus on disadvantaged groups as they are the most likely victims of dehumanization. We will investigate whether increasing the agency of these groups - by describing them as successful in their actions (compared to failures) - will reduce their dehumanization.

Grant awarded by the National Science Center to dr hab. Magdalena Formanowicz.

Scholarship

Institute of Psychology

Tasks:

• Exploring the literature of the subject,
• Developing research materials and procedures (pilots and relevant research) in cooperation with the project manager or post-doc
• Recruiting participants for research,
• Training researchers' associates,
• Supervising and/or conducting research,
• Performing data analysis and interpretation in cooperation with the PI or post-doc
• Participating in research team meetings,
• Cooperating with the project manager or post-doc in the preparation of articles presenting partial research results,
• Participating in international conferences,
• Presenting partial research results at conferences.

La conception de moteurs aéronautiques de nouvelle génération nécessite l'intégration de simulations à résolution d'échelle d'ordre élevé (SRS) et de méthodologies d'optimisation dans les flux de travail industriels standards. Les futurs concepts de moteurs nécessitent d'ouvrir l'espace de conception et de résoudre des problèmes de conception complexes hors de portée des processus de conception industrielle standard d'aujourd'hui dans les délais requis. Dans le cadre du projet collaboratif Horizon cluster 5 Sci-Fi-Turbo, nous cherchons à répondre à ce besoin urgent en exploitant les opportunités de trois technologies fondamentales : le calcul haute performance, les méthodes numériques d'ordre élevé et l'IA/ML. Cette combinaison est utilisée pour mettre en œuvre et démontrer des avancées clés dans la conception de turbomachines.

Participants : Sci-Fi-Turbo est dirigé par le Centre allemand de recherche aérospatiale DLR (Cologne) et comprend des partenaires tels que GE-Allemagne, ANSYS, Sorbonne Université, Imperial College, Université de Melbourne, CINECA, Université de Braunshweig, Chalmers.

Objectif : Modèles de turbulence à la volée basés sur des données pour une conception aérodynamique haute fidélité Les processus de conception de turbomachines industrielles s'appuient sur des solveurs (U)RANS à volumes finis du second ordre. Le changement radical requis dans la capacité prédictive a toutefois nécessité l'introduction d'approches précises de haut niveau et SRS dans le processus de conception. Néanmoins, la maturité actuelle des solveurs SRS d’ordre élevé compatibles HPC pour les applications de turbomachines n’est pas suffisante pour être utilisée dans les flux de travail de conception industrielle standard. Seul un très petit nombre de SRS peuvent être réalisés lors d’une optimisation, en raison de leur coût de calcul élevé. Pour atténuer cela, nous prévoyons d'exploiter les données SRS haute fidélité pour former et Des modèles RANS augmentés par apprentissage automatique, et plus particulièrement des modèles basés sur des données en boîte ouverte basés sur des algorithmes d'identification symbolique [1,2], pour fournir des niveaux de précision proches de SRS, au moins à proximité de l'ensemble d'entraînement. Les objectifs des travaux de thèse sont les suivants : parvenir à une formation de modèle informatiquement efficace et automatisée, pour permettre un recyclage « à la volée » lors de l'optimisation, si nécessaire ; assurer la robustesse numérique des modèles basés sur les données, qui doivent répondre à des critères de convergence stricts pour éviter les incertitudes associées aux erreurs de solution ; développer des modèles basés sur des données qui soient généralisables sur une sous-région suffisamment grande de l'espace des paramètres, et pour éviter des erreurs d'extrapolation catastrophiques ou la nécessité d'un recyclage trop fréquent. Pour relever ces défis, les approches basées sur les données sera contraint de garantir le bon comportement des modèles (invariance, symétries, réalisabilité). De plus, nous étudierons les critères de généralisation, par exemple basés sur la distance de Malahanobis ou les limites PAC-Bayes [3,4]. Ces critères seront utilisés pour mesurer la fiabilité prédictive du modèle et déclencher un recyclage si nécessaire.

Plan de travail : Les travaux de thèse seront répartis sur trois ans. Il/elle devra travailler en étroite interaction avec les autres membres de l'équipe (personnel permanent, doctorant et postoc impliqués dans Sci-Fi-Turbo) et participer aux activités du projet : interactions avec d'autres partenaires, participation aux réunions d'avancement, rédaction de rapports. Elle/Il devra également participer aux activités de diffusion en participant à des conférences internationales et en rédigeant des articles.

Le programme provisoire est le suivant : • Bibliographie sur les modèles de turbulence augmentés par apprentissage automatique et les critères de généralisation. Collecte de données de formation et de validation (y compris les données générées au sein du consortium Sci-Fi-Turbo) et génération de bases de données SRS supplémentaires si nécessaire. Enquête sur des techniques de formation informatiques efficaces et entièrement automatiques. • Enquête sur les critères d'optimalité des données d'entraînement. Formation du modèle RANS basé sur les données et évaluation sur une variété de cas de test. Enquête sur les capacités de généralisation et estimation des incertitudes prédictives. Définition de mesures d'extrapolation adaptées et de critères de reconversion. Définition d'un workflow automatique. • Application du workflow aux configurations de compresseurs Sci-Fi-Turbo et validation approfondie. Intégration au sein d'algorithmes d'optimisation multi-fidélité développés par PhD2 et application à la conception d'une aube de compresseur.

Références : [1] Schmelzer, M., Dwight, RP et Cinnella, P. (2020). Découverte de modèles algébriques de contrainte de Reynolds utilisant la régression symbolique clairsemée. Écoulement, turbulence et combustion, 104, 579-603. [2] Cherroud, S., Merle, X., Cinnella, P. et Gloerfelt, X. (2022). Apprentissage bayésien clairsemé de modèles algébriques explicites de contraintes de Reynolds pour les écoulements séparés turbulents. International Journal of Heat and Fluid Flow, 98, 109047. [3] Wu, JL, Wang, JX, Xiao, H. et Ling, J. (2017). Évaluation a priori de la confiance des prédictions pour la modélisation de la turbulence basée sur les données. Écoulement, turbulence et combustion, 99, 25-46 [4] Alquier, P. (2021). Introduction conviviale aux limites PAC-Bayes. Préimpression arXiv arXiv:2110.11216.

The National Centre for Nuclear Research (NCBJ) and the Joint Research Centre of the European Commission (JRC) are inviting applications in the field of "Non-power nuclear and radiological technologies" for a Ph.D. position through the Collaborative Doctoral Partnerships (CDP) scheme (Agreement n. 36149).

Place of work:

The Ph.D. research will be carried out jointly at the National Center of Nuclear Research Nomaten Center of Excellence 7 Andrzeja Sołtana str. 05-400 Otwock-Świerk, Poland

and

The European Commission – Joint Research Centre Nuclear Safety and Security Directorate Westerduinweg 3 1755 LE Petten, The Netherlands.

Research topic description: 

The person hired will carry out the research topic related to the project entitled "Mechanical and structural properties of RAFM steels - impact of radiation damage.", which is funded by the National Science Center under the POLONEZ BIS competition – project number 2022/47/P/ST5/01169 (project leader Dr. Tymofii Khvan).

Materials under neutron irradiation are subject to irradiation damage and degradation of their mechanical properties. However, neutron irradiation studies for research purposes are complicated, time-consuming, and expensive, so emulating neutron irradiation damage by much faster and cost-effective ion irradiation is of high interest. At the same time, ion irradiation avoids activation of the irradiated material. However, the emulation of neutron irradiation by ion irradiation damage is limited by transferability issues and experimental uncertainties linked to the limited penetration of the energetic ions. The goal of the work is to improve our understanding of the phenomena associated with the formation and evolution of ion irradiation-induced defects and their role in the deformation behavior in ferritic/martensitic (f/m) steels, incl. pure Fe, Fe9%Cr, Fe9%Cr-NiSiP and Eurofer 97. Nanoindentation will be carried out using different indenter shapes to study irradiation hardening. Radiation-induced defects will be identified by TEM. Complementary analyses will be carried out, including plasma-focused ion beam tomography of indents. The project aims at establishing and validating a comprehensive testing protocol with complementary investigations to support the experimental activities. An experimental/computational procedure is targeted for effectively characterizing ion irradiation as a surrogate for neutron irradiation. The project will also contribute to developing and validating models for size effects during nanoindentation in irradiated steels through a statistical sampling approach across a specimen surface. It is well known that size effects are influenced by irradiation; however, the origin has been debated. Micromechanical models and validation experiments will be performed that will account for geometrically necessary dislocations generated at the indent. Based on dislocation dynamics formulations in samples with various defects/defect clusters, a machine-learning approach will be developed to evaluate nanoindentation responses in irradiated metals. The Ph.D. project is expected to effectively improve experimental/computational protocols for the fast, safe, and reliable assessment of the irradiation impact on the mechanical properties of structural steels. The topic is in line with the research carried out by the NCBJ and in line with the mission of the JRC. Therefore, the Ph.D. candidate will conduct his/her research for up to 24 months at the Joint Research Centre in Petten underemployment as a grant holder through the Collaborative Doctoral Partnerships (CDP) scheme (Agreement n. 36149). The candidate will be enrolled in NCBJ Ph.D. school and awarded a Ph.D. Degree in Physics at the end of the three to four-year Programme after successfully completing the entire Ph.D. requirements, including the successful defense of the Thesis as provided by the NCBJ Ph.D. school Regulations on Ph.D. Programmes.

PhD programme: 

The duration of the Doctoral Programme is expected to be from three to four years (maximum). The planned start of the project is January 1st, 2024. It is expected that the Ph.D. candidate will join JRC in Petten (The Netherlands) during the third/fourth quarter of 2024 for up to 24 months. Afterwards, the Ph.D. candidate will return to NCBJ to continue the research and finally obtain a Ph.D. in Physics awarded by NCBJ.

We offer: 

• full-time employment paid from the project 5000 PLN/month (gross) while staying at NCBJ, during research stay at JRC potential candidate will be offered a Grant holder (cat.20) contract (https://joint-research-centre.ec.europa.eu/system/files/2014-08/jrc_gran...) and will receive 39,240 Euros per year (gross), correction factor may apply + family & mobility allowance (if applicable).
• work in the only research institute in Poland with a nuclear reactor
• the opportunity to participate in the creation of a new international unit, the chance for personal success related to the development of MAB NOMATEN;
• the opportunity for personal growth through a diverse range of tasks and challenges in the study of materials dedicated to nuclear technologies;
• the opportunity to participate in international and national scientific conferences;
• additional annual salary and a package of benefits from the Company's Social Benefits Fund (among other things, holiday subsidies)
• company transportation providing access to Świerk from many places in Warsaw and the surrounding area (detailed schedule in the "contact" tab at www.ncbj.gov.pl).

Candidates should, prior to the start of the employment contract with the JRC:

• have the nationality of a Member State of the EU or a country associated to the Research Framework Programmes or being resident in EU Member State since at least five years.
• be enrolled in a PhD programme with the HEI.

Candidates who are already enrolled in the doctoral study program with the HEI for fewer than 12 months can also be considered eligible. The selected candidate will have not more than 6 months from the request for confirmation of interest in the position, to produce proof of enrolment in the doctoral study program.

The hired person will be responsible for: 

• preparation of samples for the experiments (cutting, polishing, encapsulating)
• preparing specimens for testing in a scanning and transmission electron microscopes (SEM/TEM);
• conducting studies at room and elevated temperatures (e.g., nanoindentation)
• conducting mechanical tests (mini-tensile with DIC, microhardness)
• development of micromechanical models and validation experiments to account for geometrically necessary dislocations generated under the indent
• developing a machine-learning approach to evaluate nanoindentation responses in irradiated metals (based on dislocation dynamics formulations in samples with various defects/defect clusters)
• reporting results on a regular basis;
• cooperating with all members of the CoE;
• preparation of scientific publications.

Requirements:

• MSc degree (or equivalent) within a relevant scientific area of the Doctoral Programme (e.g., physics, material science, nuclear engineering, mechanical engineering, or equivalent) giving access to doctoral studies;
• scientific experience documented by scientific publications;
• knowledge of the physical fundamentals of TEM methods (good understanding of the basics of crystallography);
• understanding of experimental methods of solid-state physics (e.g., nanoindentation and/or tensile testing);
• Theoretical background in physics-based modelling, including numerical methods, is an advantage;
• experience in examining radiation-damaged materials or mechanical testing at high temperatures would be a plus;
• willingness to continuously acquire knowledge and improve qualifications, creativity, responsibility, good organization, ability to work in a team, and communication skills;
• excellent oral and written English language skills.

Required documents:

• a resume containing the clause "I agree to process my personal data contained in the job offer for the purposes necessary for the recruitment process (in accordance with the Law on Personal Data Protection Dz. 2002 No. 101 item 926 as amended)".
• a cover letter;
• letters of recommendation
• a copy of the diploma confirming obtaining an MSc. Degree (or equivalent)
• list of scientific publications
• self-presentation, containing a description of the candidate's scientific achievements, along with a list of publications, patents, and implementations, description of other achievements (conference presentations, internships, etc.)
• any other possible documents that may affect the assessment

We have the power of over 40,000 students and co-workers. Students who provide hope for the future. Co-workers who contribute to Linköping University meeting the challenges of the day. Our fundamental values rest on credibility, trust and security. By having the courage to think freely and innovate, our actions together, large and small, contribute to a better world. We look forward to receiving your application!

We are looking for 1–2 PhD students in AI and machine learning with a focus on large language models. The positions are funded by WASP and the students will participate in the WASP Graduate School.

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems.

The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.

Read more: https://wasp-sweden.org/

Your work assignments

As a PhD student, you devote most of your time to doctoral studies and the research projects of which you are part. Your work may also include teaching or other departmental duties, up to a maximum of 20% of full-time.

The research for this position will develop methods for making large language models more efficient by dividing them into modules that can be trained and updated independently of other model parts. Such methods are especially interesting when building models for lower-resourced languages with little training data.

Read more: https://www.ida.liu.se/divisions/aiics/nlp/phd-student/

Your workplace

You will work in the Natural Language Processing Group within the Division for Artificial Intelligence and Integrated Computer Systems (AIICS).

Read more: https://www.ida.liu.se/divisions/aiics/nlp/

The Department of Computer and Information Science was founded in 1983, but its roots go back to the early 1970s. It is one of the largest computer science departments in northern Europe. Our research covers a broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes at Bachelor’s and Master’s levels, as well as programmes in statistics, cognitive science and innovative programming.

Read more: https://liu.se/en/organisation/liu/ida

Linköping University is an AI research excellence centre with strong and broad research and education in AI from a technical and interdisciplinary perspective. LiU is the coordinator of one of the four EU-funded networks of AI research excellence centres (TAILOR) and the host of the largest basic research programme in Sweden (Wallenberg AI, Autonomous Systems and Software Program, WASP) as well as the Wallenberg AI and Transformative Technologies Education Development Program (WASP-ED). LiU also hosts the National Academic Infrastructure for Supercomputing in Sweden (NAISS), which includes an NVIDIA SuperPOD (Berzelius).

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, autonomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. The graduate school offers a unique opportunity for students who are interested in world-class international research with industrial relevance.

Read more: https://wasp-sweden.org/graduate-school/

Your qualifications

We are looking for a goal-oriented person who can work with complex issues, independently pursue their work, and collaborate with others.

You should have a Master’s degree in computer science or have completed coursework amounting to at least 240 ECTS, including 60 ECTS from second-cycle courses in computer science. Alternatively, you have gained essentially corresponding knowledge in another way. You should also have high proficiency in written and oral English communication.

You should have very good knowledge of and practical experience with machine learning and programming. Experience with large language models and research experience from undergraduate education are meritorious.

In your personal letter, we expect you to explain why you are interested in this position and consider yourself a good fit for it.

The employment

When taking up the post, you will be admitted to the program for doctoral studies. More information about the doctoral studies at each faculty is available at Doctoral studies at Linköping University

The employment has a duration of four years’ full-time equivalent. You will initially be employed for a period of one year. The employment will subsequently be renewed for periods of maximum duration two years, depending on your progress through the study plan. The employment may be extended up to a maximum of five years, based on the amount of teaching and departmental duties you have carried out. Further extensions can be granted in special circumstances.

Starting date by agreement.

Salary and employment benefits

The salary of PhD students is determined according to a locally negotiated salary progression.

More information about employment benefits at Linköping University is available here.

Union representatives

Information about union representatives, see Help for applicants.

Application procedure

Apply for the position by clicking the “Apply” button below. Your application must reach Linköping University no later than October 19, 2023.

Applications and documents received after the date above will not be considered.

We welcome applicants with different backgrounds, experiences and perspectives - diversity enriches our work and helps us grow. Preserving everybody's equal value, rights and opportunities is a natural part of who we are. Read more about our work with: Equal opportunities.

We look forward to receiving your application! Linköping university has framework agreements and wishes to decline direct contacts from staffing- and recruitment companies as well as vendors of job advertisements.