25 km
Research assistant (postdoc) (m­­/­­f­­/­­d) 04.07.2024 Freie Universität Berlin Berlin
Weitere passende Anzeigen:

Ihre Merkliste/

Mit Klick auf einen Stern in der Trefferliste können Sie sich die Anzeige merken

1

Passende Jobs zu Ihrer Suche ...

... immer aktuell und kostenlos per E-Mail.
Sie können den Suchauftrag jederzeit abbestellen.
Es gilt unsere Datenschutzerklärung. Sie erhalten passende Angebote per E-Mail. Sie können sich jederzeit wieder kostenlos abmelden.

Informationen zur Anzeige:

Research assistant (postdoc) (m/f/d)
Freie Universität Berlin
Berlin
Aktualität: 04.07.2024

Anzeigeninhalt:

04.07.2024, Freie Universität Berlin
Berlin
Research assistant (postdoc) (m/f/d)
Ihre Aufgaben:
logo Department of Mathematics and Computer Science - SFB 1114: Scale Cascades in Complex Systems Research assistant (postdoc) (m/f/d) full-time job limited to 30.06.2026 for the duration of the project salary grade (Entgeltgruppe) 13 TV-L FU reference code: SFB1114-A04-2024 CRC 1114 aims at methodological developments for the modelling and computational simulation of complex processes involving many (more than two) interacting scales, driven by real-life applications from the bio-, geo-, and material sciences. In its individual projects, mathematicians cooperate with colleagues from the natural sciences to advance both their modelling capabilities and their insight into concrete multiscale phenomena. Job description: This PostDoc position is part of the CRC1114 project A04 (Efficient calculation of slow and stationary scales in molecular dynamics) and is situated within the Al4Science group (Prof. Noé) at Freie Universität Berlin. The group develops new machine learning techniques for the molecular sciences. Aim of the project is the development of new theory and machine learning methods for the coarse-graining of quantum or classical many-body systems. These methods will be of importance for the development of drugs, materials as well as fundamental research in chemistry and biology. Requirements: BSc or MSc in Physics, Chemistry, Computer Science or related areas. Completed PhD, ideally with focus on method development in quantum Chemistry or statistical Mechanics. Desirable: Experience in the development of machine learning systems and neural networks, especially Graph Neural Networks, Transformer, invariance and equivariance, statistical learning theory. Experience in the modeling and simulation of molecular systems. Extensive programming experience in Python, and with PyTorch or JAX. Scientific publications. Fluent in written and spoken English. For further information, please contact Prof. Dr. Frank Noé (frank.noe@fu-berlin.de / +49 30 838 75354). Applications should be sent by e-mail, together with significant documents, indicating the reference code, no later than July 29th, 2024 in PDF format (preferably as one document) to Prof. Dr. Frank Noé: gs-sfb1114@math.fu-berlin.de or postal to Freie Universität Berlin Fachbereich Mathematik und Informatik SFB 1114: Skalenkaskaden in komplexen Systemen Mr. Prof. Dr. Frank Noé Arnimallee 6 14195 Berlin (Dahlem) With an electronic application, you acknowledge that FU Berlin saves and processes your data. FU Berlin cannot guarantee the security of your personal data if you send your application over an unencrypted connection. Freie Universität Berlin is an equal opportunity employer.
Das bringen Sie mit:
logo Department of Mathematics and Computer Science - SFB 1114: Scale Cascades in Complex Systems Research assistant (postdoc) (m/f/d) full-time job limited to 30.06.2026 for the duration of the project salary grade (Entgeltgruppe) 13 TV-L FU reference code: SFB1114-A04-2024 CRC 1114 aims at methodological developments for the modelling and computational simulation of complex processes involving many (more than two) interacting scales, driven by real-life applications from the bio-, geo-, and material sciences. In its individual projects, mathematicians cooperate with colleagues from the natural sciences to advance both their modelling capabilities and their insight into concrete multiscale phenomena. Job description: This PostDoc position is part of the CRC1114 project A04 (Efficient calculation of slow and stationary scales in molecular dynamics) and is situated within the Al4Science group (Prof. Noé) at Freie Universität Berlin. The group develops new machine learning techniques for the molecular sciences. Aim of the project is the development of new theory and machine learning methods for the coarse-graining of quantum or classical many-body systems. These methods will be of importance for the development of drugs, materials as well as fundamental research in chemistry and biology. Requirements: BSc or MSc in Physics, Chemistry, Computer Science or related areas. Completed PhD, ideally with focus on method development in quantum Chemistry or statistical Mechanics. Desirable: Experience in the development of machine learning systems and neural networks, especially Graph Neural Networks, Transformer, invariance and equivariance, statistical learning theory. Experience in the modeling and simulation of molecular systems. Extensive programming experience in Python, and with PyTorch or JAX. Scientific publications. Fluent in written and spoken English. For further information, please contact Prof. Dr. Frank Noé (frank.noe@fu-berlin.de / +49 30 838 75354). Applications should be sent by e-mail, together with significant documents, indicating the reference code, no later than July 29th, 2024 in PDF format (preferably as one document) to Prof. Dr. Frank Noé: gs-sfb1114@math.fu-berlin.de or postal to Freie Universität Berlin Fachbereich Mathematik und Informatik SFB 1114: Skalenkaskaden in komplexen Systemen Mr. Prof. Dr. Frank Noé Arnimallee 6 14195 Berlin (Dahlem) With an electronic application, you acknowledge that FU Berlin saves and processes your data. FU Berlin cannot guarantee the security of your personal data if you send your application over an unencrypted connection. Freie Universität Berlin is an equal opportunity employer.

Berufsfeld

Standorte