Blätter-Navigation

An­ge­bot 128 von 380 vom 11.10.2019, 12:54

logo

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

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. 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 stu­dents.

Require­ments:

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 tog­e­ther with machine learn­ing/neural net­works such as PyT­orch or Ten­sor­flow. 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 sekr@ml.tu-berlin.de.

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.