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An­ge­bot 68 von 82 vom 11.01.2021, 00:00

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

The Clem­enti's group in the Phys­ics Depart­ment at Freie Uni­versität Ber­lin seeks a postdoc­toral researcher to work on the devel­op­ment and applic­a­tion of coarse-grain­ing meth­od­o­lo­gies to study mac­ro­molec­u­lar dynam­ics with machine learn­ing and exper­i­mental data, integ­rat­ive struc­tural bio­logy, and mem­brane pro­tein mod­el­ing. 

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 mac­ro­molec­u­lar 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.

Rese­arch assi­stant (post­doc) (m/f/d)

Work­ing field:

  • 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:

  • Can­did­ates must have a Ph.D. in Phys­ics, Chem­istry, Applied Math­em­at­ics, or related fields

Desir­able:

  • Pre­vi­ous exper­i­ence with mac­ro­molec­u­lar mod­el­ing and molecu­lar dynam­ics sim­u­la­tions.

  • Eng­lish lan­guage flu­ent, spoken and writ­ten.
  • Excel­lent the­or­et­ical and prac­tical exper­i­ence with machine learn­ing meth­ods is desir­able. In par­tic­u­lar, the use of machine of machine-learn­ing to define rep­res­ent­a­tions and high-dimen­sional poten­tial energy sur­faces for atom­istic sys­tems. Exper­i­ence with deep neural net­works and ker­nel meth­ods is desir­able.

How to ap­ply:

All applic­a­tions (includ­ing a short cover let­ter out­lining your back­ground, a detailed CV includ­ing pos­sibly some ref­er­ences, and all rel­ev­ant cer­ti­fic­ates) quot­ing the ref­er­ence code should be dir­ec­ted no later than Feb­ru­ary 1st, 2021 prefer­ably elec­tron­ic­ally in one PDF-file to: Mrs. Prof. Dr. Cecilia Clem­enti: ammonlassen@physik.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)

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.