Riga Technical University

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.  The primary responsibility of this role is to deliver on an innovation research project where you will be part of the research team to conduct applied research in the topic of Human-in-the-Loop AI perception system that leverages user concerns and identified driving scenarios to generate new and relevant driving scenes for enhancing the perception system performance for autonomous vehicles (AV). Key Responsibilities Participate in and manage the research project with Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met. Undertake these responsibilities in the project: Conduct the research work in Human-in-the-Loop AI perception system for autonomous driving Work with the research team, collaborators and the industry partner to design the framework of the proposed solution Carry out Risk Assessment, and ensure compliance with Work, Safety and Health Regulations. Coordinate procurement and liaison with vendors/suppliers. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be advantageous. Have strong research record, preferably on top-tier computer science/engineering conferences. Key Competencies Specialized in computer science, computer engineering, or relevant disciplines Enthusiasm in the Deep learning and AV related research Proficient in paper writing and presentation Possess strong analytical and critical thinking skills Able to build and maintain strong working relationships with people within and external to the university Self-directed learner who believes in continuous learning and development Show strong initiative and take ownership of work

As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets that are relevant to industry demands while working on research projects in SIT.  Job Purpose The primary responsibility of this role is to deliver on an academic research project funded by BCA. The project is in collaboration with IHL and an industry partner. where you will be part of the research team to develop and demonstrate Building-to-Grid Integration through Intelligent Optimization and Predictive Control at SIT’s Punggol campus. Under this job title, you will be required to establish a test bed in SIT’s Punggol campus building, benchmarking of current building energy performances, deployment of new sensors & hardware, and quantitative evaluation of energy savings. You will be required to coordinate with multiple internal and external parties for the successful deployment of technology, test-bedding, and energy performance evaluation. Key Responsibilities Participate in and manage the research project with the Principal Investigator (PI), Co-PI and the research team members to ensure all project deliverables are met. Candidate will be required to establish test bed in SIT’s Punggol campus building, benchmarking of current building energy performances, deployment of new sensors, hardware, quantitative evaluation of energy savings, etc. Meeting the deliverables by deployment, demonstration, and experimental studies planned for under the scope of work of the projects within the timeframe to meet the deliverables. Coordination with project internal and external team members, vendors, contractors, safety officers, for successful deployment of technology and testing. Assists in the co-supervision of final year projects (FYP) or capstone projects students under the Project PI. Assists the PI in drafting project reports, conference proceedings, and journal articles based on the outcome of the projects. Prepares and shares a fortnightly report of results from the computations with the PI. Carry out Risk Assessment, and ensure compliance with Work, Safety, and Health Regulations. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment. Job Requirements PhD degree in electrical/civil/mechanical engineering or other related fields Expert knowledge of building air-conditioning and mechanical ventilation, simulation, building automation and control, sensor network, thermal energy storage, indoor air quality, heat transfer, and building energy performance evaluation.  Experience of test bed setup development, testing & commissioning, and performance analysis. Knowledge of industrial protocols, sensors, and control hardware. Knowledge of building management systems, software integration, data collection, and analysis. Industry or research lab experience is required. Strong analytical and conceptual abilities. Ability to work in a team as well as independently. Able to work under pressure and meet deadlines. Key Competencies  Able to build and maintain strong working relationships with people within and external to the university. Able to work independently with strong data analytical skills, communication and interpersonal skills. Self-directed learner who believes in continuous learning and development Proficient in technical writing and presentation Possess strong analytical and critical thinking skills Show strong initiative and take ownership of work

At the University of Vienna more than 10,000 personalities work together towards answering the big questions of the future. Around 7,500 of them do research and teaching, around 2,900 work in administration and organisation. We are looking for a/an University assistant predoctoral 51 Faculty of Physics Startdate: 01.02.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1 Grundstufe (praedoc) Limited until: 31.01.2029 Reference no.: 5031 Explore and teach at the University of Vienna, where more than 7,500 academics thrive on curiosity in continuous exploration and help us better understand our world. Does this sound like you? Then join our accomplished team! About the team: This is an opportunity to work towards a PhD in physics and to conduct world-leading research and teaching in molecular simulation and computational materials discovery. Fuel cells, photovoltaic devices, photocatalytic converters – they all are crucial elements in delivering decarbonization and sustainable energy production at a global scale within the coming decades. They all fundamentally involve energy transfer and chemical dynamics at interfaces where molecules, electrons, and light interact to deliver a certain function. The underlying mechanisms of ultrafast dynamics at surfaces triggered by light or electrons are not well understood, which, for example, limits our ability to design photocatalyst materials that deliver optimal light absorption, catalytic activity, and energy transport. Molecular simulation methods and quantum theoretical calculations in principle can address this but have hitherto struggled with tackling such challenging systems. With the emergence of machine learning methods in the physical sciences, things are rapidly changing. This project is part of a large initiative that aims to tackle this ambitious challenge by developing and applying new software tools that combine machine learning methodology, electronic structure theory, and molecular dynamics methodology to simulate ultrafast chemical dynamics at surfaces and in materials. Your personal sphere of influence: As a university assistant (praedoc), you will be part of the Computational Materials Discovery group of Professor Reinhard Maurer. This project will focus on the development of novel machine learning representations of electronic structure and quantum operators. These surrogate models will be combined with mixed quantum-classical dynamics simulation approaches to perform chemical dynamics simulations at an unprecedented scale. You will contribute to the development of broadly applicable electronic structure methods, new software, and machine learning methods with the specific goal to enable high throughput screening of optimal photocatalyst materials and to reveal important mechanistic insights into ultrafast dynamics and measurable spectroscopic properties. This will be done in close collaboration with a broad network of international collaborators. The employment duration is 3 years. Initially limited to 1.5 years, the employment relationship is automatically extended to 3 years if the employer does not terminate it within the first 12 months by submitting a non-extension declaration.With appropriate work progress, an extension to a total maximum of 4 years is possible. Your future tasks: You will actively participate in research, teaching & administration. This means: You are involved in a curiosity-driven research project in the field of machine learning in computational materials science. You will present your research plan to the faculty and complete a dissertation agreement within 12-18 months. This will be reviewed and adapted on an annual basis. You will work on your dissertation and towards its completion in time. We expect a large degree of independence paired with a high level of social awareness as the goal will only be achieved in a team. You will contribute to teaching (exercise classes) within the provisions of the collective bargaining agreement. You will fill some administrative tasks, contributing to the success and self-organization of the group for research, teaching and administration. You will continuously stay informed about the state of the art in your field. You will contribute to outreach by publications, conference presentations and public activities. Candidate profile: You have completed your Master's degree or Diploma in physics. You have experience in academic writing. You have an interest and background in condensed matter theory and electronic structure theory. You have an excellent command of written and spoken English. You should have experience with programming (e.g. Python, Julia), simulation methods (e.g. molecular dynamics) and modern machine learning methods. What we offer: In the Maurer group, we aim to develop computational simulation methodology to study quantum phenomena at surfaces with applications ranging from photocatalysis to nanotechnology and electrochemistry. Our goal is to combine electronic structure theory, molecular and quantum dynamics methodology, and machine learning methods to achieve an accurate yet computationally feasible description of complex phenomena in materials and at solid/gas and solid/liquid interfaces. You will join a large, international and interdisciplinary research group that provides a collaborative and supportive environment. Our team is member of the Vienna Doctoral School in Physics and the faculty research group Computational Materials Physics. PhD students in the group acquire important transferable skills such as software development and project management. You will present your research at international and national conferences. Inspiring working atmosphere: You are a part of an international academic team in a healthy and fair working environment. Potential for development: Success in life depends on what you make of it, but if you are ambitious and self-driven, there are plenty of opportunities to connect you to all relevant top research groups in the world. Good public transport connection:  Your new workplace is easily accessible by public transport. Internal further training & Coaching: The Vienna Doctoral School as well as the Department of Human Resources offer plenty of opportunities to grow your skills in over 600 courses to choose from – free of charge. Fair salary: The basic salary of EUR 3.776,10 (on a full-time basis, 14x p.a.) increases if we can credit professional experience. Equal opportunities for everyone: We strive to create a fair and equitable work environment, where diversity is an asset and individuals can flourish. It is that easy to apply: Your scientific curriculum vitae. A summary of your previous academic and research achievements. Tell us about your moments of professional pride. A short statement on your research interests for the future (motivation letter). Tell us what you dream about, scientifically. Your Bachelor’s and Master's degree: an excellent academic degree is a good entrance statement for this position. Note, that while a Master’s degree is the usual legal prerequisite for a PhD position at the University of Vienna, we can consider Bachelors (preferentially with Honors) in exceptional cases, too. If you have any content questions, please contact: Reinhard Maurer reinhard.maurer@univie.ac.at We look forward to new personalities in our team! The University of Vienna has an anti-discriminatory employment policy and attaches great importance to equal opportunities, the advancement of women and diversity. We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general university staff and therefore expressly encourage qualified women to apply. Given equal qualifications, preference will be given to female candidates. University of Vienna. Space for personalities. Since 1365. Data protection Application deadline: 01/30/2026 Prae Doc