An­ge­bot 241 von 635 vom 11.07.2018, 11:32


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

Research Assist­ant (PostDoc) - salary grade E14 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, i.e. devel­op­ment of deep neural net­works and applic­a­tion thereof to com­plex, het­ero­gen­eous data such as graphs and trees. Devel­op­ment of robust and inter­pretable mod­els incor­por­at­ing prior know­ledge from the applic­a­tion domains. Advance­ment of meth­ods to inter­pret and explain the pre­dic­tions of deep neural net­works. Super­vi­sion of Bach­elor/Mas­ter/PhD can­did­ates.


Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) und doc­toral degree in com­puter sci­ence, math­em­at­ics or phys­ics. Sev­eral years of exper­i­ence as sci­entific research assist­ant in the field of machine learn­ing. Extens­ive and deepened know­ledge on: meth­ods and the­ory of machine learn­ing, deep neural net­works, explan­a­tion meth­ods, inter­pretable mod­els, applic­a­tion of machine learn­ing meth­ods on real-world high-dimen­sional data: regres­sion, clas­si­fic­a­tion, and clus­ter­ing as well as their empir­ical eval­u­ation.
Very good pro­gram­ming skills and expert­ise in using math­em­at­ical soft­ware and sim­u­la­tion envir­on­ments such as Python or Mat­lab together with object-ori­ented lan­guages like Java or C++ are man­dat­ory. Exper­i­ences in inter­dis­cip­lin­ary research as well as pub­lic­a­tions in machine learn­ing journ­als and/or con­fer­ences are desir­able. Very good com­mand of Ger­man and Eng­lish is required.

How to ap­ply:

Please send your writ­ten applic­a­tion with the ref­er­ence num­ber and the usual doc­u­ments to Tech­nis­che Uni­versität Ber­lin - Der Präsid­ent - Fakultät IV, Institut für Softwaretechnik und Theoretische Informatik, FG Maschinelles Lernen, Prof. Dr. Müller, Sekr. MAR 4-1, Marchstr. 23, 10587 Berlin or by e-mail to

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.

Please send cop­ies only. Ori­gi­nal docu­ments will not be retur­ned.