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

Research Assist­ant - salary grade E13 TV-L Ber­liner Hoch­schu­len - 1st qual­i­fic­a­tion period (PhD can­did­ate)

part-time employ­ment may be pos­sible

Work­ing field:

Research tasks in the field of Machine Learn­ing, devel­op­ment of ana­lysis meth­ods of non­station­ary data in par­tic­u­lar. Advance­ment of robust and addit­ive meth­ods to solve stat­ist­ical learn­ing prob­lems. Stat­ist­ical Ana­lysis of multi-dimen­sional sensor data as well as autonom­ous exe­cu­tion of exper­i­ments includ­ing but not lim­ited to the field of neuro sci­ences ( EEG and EMG-data). Imple­ment­a­tion, applic­a­tion and eval­u­ation of developed tech­niques.
Super­vi­sion of Bach­elor/Mas­ter stu­dents. Teach­ing in this sub­ject area.


Suc­cess­fully com­pleted uni­versity degree (Mas­ter, Dip­lom or equi­val­ent) in math­em­at­ics or com­puter sci­ences. Ideally, sev­eral years of exper­i­ence as sci­entific research assist­ant in the field of machine learn­ing. Extens­ive and deepened know­ledge on: stat­ist­ical learn­ing the­ory and machine learn­ing meth­ods, ker­nel meth­ods, neural net­works, prob­ab­il­ity the­ory, stat­ist­ics (Bayes‘ stat­ist­ics, Bayes‘ optim­iz­a­tion, vari­ational meth­ods et al.), non-lin­ear optim­iz­a­tion, applic­a­tion of machine learn­ing meth­ods on high-dimen­sional data: regres­sion-, clas­si­fic­a­tion- and clus­ter­ing prob­lems 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 indus­trial pro­ject work 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.