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Tech­ni­sche Uni­ver­sität Ber­lin - Fac­ulty IV - Insti­tute of Soft­ware Engin­eer­ing and The­or­et­ical Com­puter Sci­ence / Data­base Sys­tems and Inform­a­tion

Rese­arch Assist­ant (Post­Doc) - salary grade E13 TV-L Ber­liner Hoch­schu­len - 2nd qual­i­fic­a­tion period

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

Work­ing field:

The sys­tem archi­tec­ture of data­base man­age­ment sys­tem (DBMS) is rather com­plex, with query pro¬ces­sor and stor­age man­age­ment as essen­tial com­pon­ents. Due to het­ero­gen­eous hard­ware and com­plex require­ments in terms of query lan­guages, data types and sca­lab­il­ity, the com­plex­ity of these com­pon­ents con­tin­ues to increase. The aim of this rese­arch pro­ject is to invest­ig­ate the reduc­tion of the com­plex­ity of a DBMS through the “Machine Learn­ing (ML) for Sys­tems” or “Soft­ware 2.0” approach. The fun­da­men­tal rese­arch ques­tion we strive to ans­wer are “Which com­pon­ents of a data­base sys­tem can be repla­ced or impro­ved by ML meth­ods?” and “How should such a sys­tem be archi­tec­ted?” Pre­vi­ous suc­cess­ful rese­arch has achie­ved break­throughs through learn­ing query optim­izers or lear­ned inde­xes. In this pro­ject, we strive to deve­lop ML algo­rithms and mod­els for com­pon­ents of a data­base sys­tem, integ­rate them into an open-source sys­tem archi­tec­ture, and demon­strate their effi­ciency and effect­ive­ness. This pos­i­tion inclu­des teach­ing and enab­les fur­ther qual­i­fic­a­tion through post­doc­toral achieve­ments (equi­val­ent to a Habil­it­a­tion).


Suc­cess­fully com­ple­ted uni­ver­sity degree (Mas­ter, Dip­lom or equi­val­ent) and PhD in Data­base Man­age­ment Sys­tems. They should be inter­ested in devel­op­ing a novel sys­tem and con­duct rese­arch in a real-world app­lic­a­tion set­ting. Ide­ally, app­lic­ants should pos­sess know­ledge in sys­tems pro­gram­ming, data man­age­ment, machine learn­ing and com­puter archi­tec­ture. App­lic­ants exper­i­enced in open source DBMS such as Post­gres or MySQL, or data pro­ces­sing sys­tems such as Apa­che Cal­cite, Flink or Spark will be loo­ked upon favor­ably. In addi­tion, flu­ency in Ger­man and good know­ledge of Eng­lish are requi­red. Moreo­ver, past open-source pro­jects, indus­try exper­i­ence, pro­ject man­age­ment, or teach­ing exper­i­ence will be loo­ked upon favor­ably.

How to ap­ply:

Please send your app­lic­a­tion with the ref­er­ence num­ber and the usual doc­u­ments by email (sin­gle pdf file, max. 5 MB) to Prof. Dr. Vol­ker Markl, (

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 the TU staff depart­ment home­page: or 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 Daten­bank­sys­teme und Inform­a­tions­man­age­ment (DIMA), Prof. Dr. Vol­ker Markl, Sekr. E-N 7, Ein­stein­ufer 17, 10587 Ber­lin