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Tech­ni­sche Uni­ver­sität Ber­lin - Fac­ulty IV - Insti­tute of Soft­ware Engin­eer­ing and The­or­et­ical Com­puter Sci­ence / Machine Learn­ing

Research Assist­ant-PostDoc - salary grade E 14 TV-L Ber­liner Hoch­schu­len

under the reserve that funds are gran­ted - part-time employ­ment may be pos­sible

Work­ing field:

  • Research tasks in the field of machine learn­ing applied to chem­ical prob­lems, i.e. devel­op­ment of deep neural net­works and ker­nel meth­ods for the pre­dic­tion of prop­er­ties of atom­istic sys­tems, e.g. poten­tial energy sur­faces for molecu­lar dynam­ics sim­u­la­tions.
  • Devel­op­ment of robust and scal­able mod­els incor­por­at­ing all phys­ical invari­ances of atom­istic sys­tems.
  • Advance­ment of meth­ods to inter­pret and explain the pre­dic­tions of atom­istic deep neural net­works. - Super­vi­sion of Bach­elor/Mas­ter/PhD stu­dents.


  • Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) and doc­toral degree in chem­istry, phys­ics or com­puter sci­ences.
  • Extens­ive and deepened know­ledge on: quantum chem­istry, molecu­lar dynam­ics sim­u­la­tions, meth­ods and the­ory of machine learn­ing, ker­nel meth­ods, deep neural net­works, and applic­a­tion of machine learn­ing meth­ods on high-dimen­sional data for chem­ical regres­sion tasks, as well as their empir­ical eval­u­ation.
  • Very good pro­gram­ming skills, for example in Python, C++ or For­tran90, as well as expert­ise with frame­works for machine learn­ing / neural net­works such as PyT­orch or Tensor­flow are man­dat­ory. Exper­i­ences with quantum chem­istry pro­grams for per­form­ing ab ini­tio cal­cu­la­tions such as GAUS­SIAN, MOLPRO or ORCA are desired.
  • Exper­i­ences in inter­dis­cip­lin­ary research as well as pub­lic­a­tions in the­or­et­ical chem­istry journ­als and/or con­fer­ences are desired.
  • Good com­mand of Ger­man and/or Eng­lish required; will­ing­ness to learn either Eng­lish or Ger­man is expec­ted.

How to ap­ply:

Please send your applic­a­tion with the ref­er­ence num­ber and the usual doc­u­ments only via email to

By sub­mit­ting your applic­a­tion via email you con­sent to hav­ing your data elec­tron­ic­ally pro­cessed and saved. Please note that we do not provide a guar­anty for the pro­tec­tion of your per­sonal data when sub­mit­ted as unpro­tec­ted file. Please find our data pro­tec­tion notice acc. DSGVO (Gen­eral Data Pro­tec­tion Reg­u­la­tion) at TU web­site quick access 214041.

To ensure equal oppor­tun­it­ies between women and men, applic­a­tions by women with the required qual­i­fic­a­tions are expli­citly desired. Qual­i­fied indi­vidu­als with dis­ab­il­it­ies will be favored. The TU Ber­lin val­ues the diversity of its mem­bers and is com­mit­ted to the goals of equal oppor­tun­it­ies.

Tech­nis­che Uni­versität Ber­lin - Der Präsid­ent - Fak­ultät IV,
Insti­tut für Soft­ware­tech­nik und The­or­et­ische Inform­atik, FG Maschinelles Lernen
Prof. Dr. K.-R. Müller, Sekr. MAR 4-1,
March­str. 23, 10587 Ber­lin