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Offer 393 out of 584 from 07/11/22, 00:00

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Freie Uni­versität Ber­lin - Fachbereich Physik - Insti­tut für The­or­et­ische Physik - AG Clem­enti - The­or­et­ische und rech­nergestützte Bio­physik

Research Assist­ant (Prae­doc) (m/f/d)

English

3/4-part-time job
lim­ited to 31.05.2025 (Sub­ject to fund­ing approval)
Salary group 13 TV-L FU
Ref­er­ence code: Ein­stein_2023_AG_Clem­enti

Working field:

The Clem­enti's group in the Phys­ics Depart­ment at Freie Uni­versität Ber­lin seeks a pre­doc­toral
researcher to work on the devel­op­ment and applic­a­tion of coarse-grain­ing meth­od­o­lo­gies to study macro-molecu­lar dynam­ics with machine learn­ing. Our group works on the defin­i­tion and imple­ment­a­tion of strategies to study com­plex bio­phys­ical pro­cesses on long times­cales. We use data-driven meth­ods for sys­tem­atic coarse-grain­ing of macro-molecu­lar sys­tems, to bridge molecu­lar and cel­lu­lar scales. We work on a the­or­et­ical for­mu­la­tion to exploit the com­ple­ment­ary inform­a­tion that can be obtained in sim­u­la­tion and exper­i­ment, to com­bine the approx­im­ate but high-res­ol­u­tion struc­tural and dynam­ical inform­a­tion from com­pu­ta­tional mod­els with the “exact” but lower res­ol­u­tion inform­a­tion avail­able from exper­i­ments.

Job descrip­tion:
  • use of machine learn­ing approaches for trans­fer­able coarse-grained mod­els of pro­teins and applic­a­tion to pro­tein fold­ing sys­tems (WP7)
  • applic­a­tion of spe­cially developed approaches to define coarse-grained pro­tein mod­els with machine learn­ing (WP8)
The pro­ject is part of the research of Clem­enti's group sup­por­ted by the Ein­stein Found­a­tion Ber­lin.
The can­did­ate will use machine learn­ing approaches (both deep neural net­work archi­tec­tures and ker­nel meth­ods) to design rep­res­ent­a­tions and trans­fer­able energy mod­els for pro­teins. Dif­fer­ent res­ol­u­tions will be explored. The mod­els will then be used to study spe­cific pro­tein sys­tems in col­lab­or­a­tion with exper­i­mental groups.

Requirements:

Require­ments:
Com­pleted sci­entific stud­ies and Uni­versity degree (M.Sc. or equi­val­ent) in Phys­ics.

Desir­able:
Solid back­ground in the­or­et­ical phys­ics, and pre­vi­ous exper­i­ence with the devel­op­ment and ana­lysis of com­plex net­work mod­els and dimen­sion­al­ity reduc­tion tech­niques. Good know­ledge of object-ori­ented pro­gram­ming lan­guages (C++ and Phyton). Eng­lish lan­guage flu­ent, spoken and writ­ten (C1)

How to apply:

Applic­a­tions should be sent by e-mail, together with sig­ni­fic­ant doc­u­ments, indic­at­ing the ref­er­ence code, in PDF format (prefer­ably as one doc­u­ment) no later than Novem­ber 28th, 2022 to Mrs. Prof. Dr. Cecilia Clem­enti: c.cuevas.alonso@fu-berlin.de or postal to

Freie Uni­versität Ber­lin
Fachbereich Physik
Insti­tut für The­or­et­ische Physik
AG Clem­enti - The­or­et­ische und rech­nergestützte Bio­physik
Mrs. Prof. Dr. Cecilia Clem­enti
Arn­im­al­lee 14
14195 Ber­lin (Dah­lem)

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