Jobs at SCC

• Research Fellow (f/m/d) - for the part-time PhD on the topic: Spatio-temporal Statistics for the Energy Transition and Transport / part-time 75% -
• Akademische Mitarbeiterin / Akademischer Mitarbeiter Scientific Computing & Simulation (w/m/d)
• PhD-Student (m/f/d) Uncertainty quantification in multiscale materials modelling - PhD Position / 75% part-time -
• Postdoctoral Research Fellow (f/m/d) - Focus on: Probabilistic Learning & Methods for Big Data -

Research Fellow (f/m/d)
- for the part-time PhD on the topic: Spatio-temporal Statistics for the Energy Transition and Transport / part-time 75% -

Category

Job description

The Scientific Computing Center is the Information Technology Center of KIT.

The newly established Research Group Methods for Big Data at Scientific Computing Center is the Information Technology Center of KIT welcomes applications for a research assistant within the Collaborative Research Center (CRC) TRR 391 "Spatio-temporal Statistics for the Energy Transition and Transport" in cooperation with the Ruhr University Bochum in the newly approved Transregio (TRR) 391, statistical methodologies are being developed to model and forecast data on the energy and mobility transition.

The position is assigned to the newly founded Collaborative Research Center (SFB) TRR 391 "Space-time statistics for the energy energy transition and transportation", which is funded by the German Research Foundation (DFG). As part of the project, a further Bochum, which will be jointly supervised by Prof. Dette (Bochum) and Prof. Klein (Karlsruhe). 

In this project you will:

• develop novel methods that leverage the potential of statistical testing and Bayesian statistics to induce sparse yet flexibly varying dependence structures between multiple outcomes that are observed over space and time
• investigate of theoretical properties of the developed methods
• implement and test the models
• public of the results at workshops and conferences

This position offers the possibility to acquire a PhD degree. Details on the research group can be found at https://kleinlab-statml.github.io/

Personal qualification

Job requirements:

• you hold a degree (Master's level) with a focus on statistics or data science or alternatively mathematics, computer science, economics, or related programs (with a focus on statistics or data science).
• Basic knowledge and initial experience with flexible regression methods, Bayesian statistics, Copulas or statistical testing
• Solid programming skills in any scientific programming language, e.g. Python, R
• Strong computational, mathematical skills
• High proficiency in English, both written and spoken for your scientific publications and presentations
• High degree of creativity, commitment, analytical competence, and interdisciplinary teamwork

We offer you an exciting and varied job within an agile team as well as a wide range of training opportunities and flexible and family-friendly working time models. For more information about SCC as your new work home, please visit https://www.scc.kit.edu/en/aboutus/working-at-scc.php

We are looking forward to your application (Motivations letter / CV / Certificates)!

Salary

Salary category 13, depending on the fulfillment of professional and personal requirements.

Akademische Mitarbeiterin / Akademischer Mitarbeiter Scientific Computing & Simulation (w/m/d)

Category

Job description

Das Scientific Computing Center ist das Informationstechnologie-Zentrum des KIT.

Zu Ihren Aufgaben zählen:

• Leitung der Abteilung SCS inkl. Führung und Koordination der Arbeiten innerhalb der Abteilung und deren strategische Ausrichtung im Zusammenwirken mit nationalen und internationalen HPC-Initiativen.
• Strategische Ausrichtung, Koordination und Weiterentwicklung des HPC-Softwareteams
• Einwerbung und Durchführung von (Drittmittel)Projekten, Finanzplanung und -controlling landes- und bundesfinanzierter HPC-Infrastrukturen, Koordination von Evaluationsvorhaben laufender und abgeschlossener HPC-Projekte und -Infrastrukturen.
• Betreuung von Softwareprodukten inkl. Verhandlung mit Anbietern und Herstellern sowie Initiierung der Beschaffung

Personal qualification

Sie bringen mit:

• Abgeschlossenes Hochschulstudium (Diplom (Uni)/Master) in einem MINT-Fach mit abgeschlossener Promotion
• Langjährige Berufserfahrung (min. 10 Jahre) im HPC-Umfeld zwingend erforderlich
• Langjährige Führungserfahrung Gruppenleitung (min. 6 Jahre) durch Teamleitung / im Bereich HPC setzen wir voraus
• Langjährige Führungserfahrung (min. 10 Jahre) von IT-Projekten in standortübergreifenden HPC-Verbünden sowie Koordinationserfahrung bei national bzw. international geführten Evaluationsvorhaben von standortübergreifenden HPC-Infrastrukturen.
• Fachkenntnisse in der HPC-Software- und HPC-Nutzenden-Betreuung (Lizenzverhandlungen, Bereitstellungsmodalitäten, Nutzendenberatung und -weiterbildung) sind unabdingbar 
• Sehr gute Englischkenntnisse (Wort /Schrift)

Wir im SCC ermöglichen Ihnen eine spannende und abwechslungsreiche Tätigkeit innerhalb eines agilen Teams sowie ein breitgefächertes Fortbildungsangebot und flexible und familienfreundliche Arbeitszeitmodelle. Information zum SCC als Ihre neue berufliche Heimat erfahren Sie auf KIT - SCC - Über uns - Arbeiten am SCC

Wir freuen uns auf Ihre Bewerbung (Motivationsschreiben / Lebenslauf/ Zeugnisse)!

Entgelt

EG 15, sofern die fachlichen und persönlichen Voraussetzungen erfüllt sind. 

PhD-Student (m/f/d) Uncertainty quantification in multiscale materials modelling
- PhD Position / 75% part-time -

Category

Job description

The Scientific Computing Center is the Information Technology Center of KIT.

This research topic deals with uncertainty quantification of multiscale materials modeling methods.

• In particularly, you will the coupling of atomistic molecular dynamics with continuum modeling and density functional theory with coarse-grained kinetic Monte Carlo will be investigated in the cases of the following two applications.
• They use the first application "Friction at Solid-Liquid Interfaces", where molecular dynamics is coupled with a continuum solver, methods for local and global sensitivity analysis need to be developed to determine and discriminate the errors caused by the molecular dynamics simulations.
• In the second application, "Single-site catalysis in porous materials", the propagation of different types of uncertainties from density functional theory calculations is investigated by coupled kinetic Monte Carlo (KMC) simulations using global sensitivity analysis.

This position offers the possibility to acquire a PhD degree. This thesis will be carried out in the framework of the interdisciplinary Research Training Group 2450 “Tailored Scale-Bridging Approaches to Computational Nanoscience” in close collaboration with partner PhD students within the training group.

Personal qualification

• You have a degree (Master's level) in mathematics or a compatible STEM subject
• Knowledge or experience in at least one of the following methodological areas is an advantage:
  • mathematical modeling
  • machine learning / statistical modeling
  • quantification of uncertainty

We offer you an exciting and varied job within an agile team as well as a wide range of training opportunities and flexible and family-friendly working time models. For more information about SCC as your new work home, please visit https://www.scc.kit.edu/en/aboutus/working-at-scc.php

We are looking forward to your application (Motivations letter / CV / Certificates)!

Salary

Salary category 13, depending on the fulfillment of professional and personal requirements.

Postdoctoral Research Fellow (f/m/d)
- Focus on: Probabilistic Learning & Methods for Big Data -

Category

Job description

The Scientific Computing Center is the Information Technology Center of KIT.

The newly established Research Group Methods for Big Data starting in August 2024 at the Scientific Computing Center welcomes applications for a postdoctoral research fellow (f/m/d)

In this project you will:

• develop novel methods that leverage the potential of statistical learning and Bayesian (Deep) Learning
• investigate of theoretical properties of the developed algorithms
• implement the developed methods
• contribute to joint research inside the group
• publish the results at leading journals, workshops and conferences

Details on the research group can be found at https://kleinlab-statml.github.io/

Personal qualification

Job requirements:

• Excellent university degree (master) and completed PhD in statistics, mathematics, data science, computer science, or similar programs
• Expertise in at least one of the following areas: Statistical Learning, Bayesian (Deep) Learning, Uncertainty Quantification and Probabilistic Machine Learning, High-Dimensional Statistics, Distributional Regression, Causal Inference, Continual Learning, Hybrid Algorithms
• Track record and excellent publications in at least one of the main research areas above
• Strong computational, mathematical skills
• Solid programming skills in any scientific programming language, e.g. Python, R
• High proficiency in English, both written and spoken for your scientific publications and presentations
• High degree of creativity, commitment, analytical competence, and interdisciplinary teamwork

We offer you an exciting and varied job within an agile team as well as a wide range of training opportunities and flexible and family-friendly working time models. For more information about SCC as your new work home, please visit https://www.scc.kit.edu/en/aboutus/working-at-scc.php.

We are looking forward to your application (Motivational letter / CV / Certificates)!

Salary

Salary category 13, depending on the fulfillment of professional and personal requirements.