Uni­versity Pro­fessor in Machine Learn­ing and Com­mu­nic­a­tion

Berlin Institute of Technology Fraunhofer Insti­tute for Tele­com­mu­nic­a­tions

Berlin Institute of Technology

Tech­nis­che Uni­versität Ber­lin, Fac­ulty IV - Elec­trical Engin­eer­ing and Com­puter Sci­ence, the Insti­tute for Soft­ware Engin­eer­ing and The­or­et­ical Com­puter Sci­ence and the Fraunhofer Insti­tute for Tele­com­mu­nic­a­tions, Hein­rich Hertz Insti­tute, are look­ing for applic­a­tions for joint appoint­ment within the frame­work of the reim­burse­ment model (Ber­lin Model) for a period of five years.

Uni­versity Pro­fessor - salary grade W2 (tem­por­ary)

for the chair "Machine Learn­ing and Com­mu­nic­a­tion“

asso­ci­ated with the head of the "Machine Learn­ing" depart­ment of the Fraunhofer Insti­tute for Tele­com­mu­nic­a­tions, Hein­rich Hertz Insti­tute HHI.

Tech­nis­che Uni­versität Ber­lin is one of the largest, inter­na­tion­ally renowned and tra­di­tional tech­nical uni­versit­ies in Ger­many. Its efforts to increase know­ledge and tech­no­lo­gical pro­gress are based on the prin­ciples of excel­lence and qual­ity.

Together with the Fraunhofer Insti­tute for Tele­com­mu­nic­a­tions, Hein­rich Hertz Insti­tute HHI, the Tech­nis­che Uni­versität Ber­lin con­ducts applied research and devel­op­ment in the future field of machine learn­ing and com­mu­nic­a­tion.

The Fraunhofer Insti­tute for Tele­com­mu­nic­a­tions, Hein­rich Hertz Insti­tute HHI is a world leader in the research of mobile and optical com­mu­nic­a­tion net­works and sys­tems and thus con­trib­utes sig­ni­fic­antly to the stand­ards for inform­a­tion and com­mu­nic­a­tion tech­no­lo­gies. Fraunhofer HHI researches the entire spec­trum of digital infra­struc­ture, from meas­ure­ment and rep­res­ent­a­tion to trans­port and eval­u­ation of sig­nals.

Sup­por­ted by a con­stantly grow­ing num­ber of avail­able train­ing data and suit­able com­puter archi­tec­tures, machine learn­ing (ML) is increas­ingly reach­ing the poten­tial of human per­form­ance and has already become an indus­trial stand­ard in some areas such as image, text and speech pro­cessing. Machine learn­ing is also used in the field of mobile net­works for a vari­ety of optim­iz­a­tion meth­ods. In this area of applic­a­tion, machine learn­ing will in all like­li­hood have a form­at­ive influ­ence in the future and will raise com­pletely new research ques­tions of its own.

Work­ing field:

In your role as Pro­fessor for Machine Learn­ing and Com­mu­nic­a­tion, you will explore the the­or­et­ical and meth­od­o­lo­gical fun­da­ment­als of machine learn­ing. You will fur­ther develop exist­ing meth­ods (e.g. deep learn­ing meth­ods) as well as new mod­els and archi­tec­tures adap­ted to the respect­ive (com­mu­nic­a­tion) applic­a­tion (dis­trib­uted learn­ing, edge com­put­ing, image and video com­mu­nic­a­tion). Prac­tic­ally rel­ev­ant char­ac­ter­ist­ics such as reli­ab­il­ity, effi­ciency and trans­par­ency of these meth­ods are the focus of the research activ­it­ies.

You will bring with you exper­i­ence with deep learn­ing meth­ods and their applic­a­tion in sig­nal pro­cessing and com­mu­nic­a­tion as well as with inter­dis­cip­lin­ary cooper­a­tion in this field. Excel­lent research achieve­ments and teach­ing exper­i­ence in the areas of decent­ral­ized machine learn­ing, inter­pretab­il­ity and com­pres­sion of ML mod­els and the applic­a­tion of ML in mobile com­mu­nic­a­tion and image and video com­mu­nic­a­tion are expec­ted.

The teach­ing oblig­a­tion at the Tech­nis­che Uni­versität Ber­lin is 2 SWS.

In your role as head of the "Machine Learn­ing" work­ing group at Fraunhofer HHI, you will be respons­ible for the sci­entific, tech­nical and entre­pren­eur­ial con­trol and devel­op­ment of the group within the Fraunhofer model and the Fraunhofer over­all strategy. Exper­i­ence in the stra­tegic plan­ning, acquis­i­tion and imple­ment­a­tion of national and inter­na­tional research and devel­op­ment pro­jects as well as com­pet­en­cies to increase the effi­ciency of devel­op­ment pro­cesses and in tech­no­logy exploit­a­tion are advant­age­ous.

You should be able to com­pet­ently rep­res­ent the main top­ics in research and teach­ing as well as in research and tech­no­logy man­age­ment vis-à-vis research spon­sors and research part­ners and to expand the stra­tegic link between the uni­versity and the Fraunhofer Insti­tute.

Require­ments:

Ful­fil­ment of the require­ments for appoint­ment accord­ing to § 100 Ber­lin Higher Edu­ca­tion Act. This includes in par­tic­u­lar a com­pleted uni­versity degree, qual­i­fied achieve­ments in research (gen­er­ally proven by PhD), addi­tional research archieve­ments (Habili­ation, post-doc­toral teach­ing, or equi­val­ent qual­i­fic­a­tion) as well as ped­ago­gical suit­ab­il­ity, rep­res­en­ted or proven by a teach­ing port­fo­lio (more inform­a­tion on this on the TUB web­site, dir­ect access 144242).

Non-Ger­man-speak­ing applic­ants are expec­ted to com­mit them­selves to learn­ing the Ger­man lan­guage quickly. Good know­ledge of Eng­lish is desir­able.

How to ap­ply:

The Tech­nis­che Uni­versität Ber­lin aims to increase the pro­por­tion of women in research and teach­ing and there­fore expressly invites qual­i­fied female sci­ent­ists to apply. Severely dis­abled applic­ants will be given pref­er­en­tial con­sid­er­a­tion if they are equally qual­i­fied. The TU Ber­lin appre­ci­ates the diversity of its mem­bers and pur­sues the goals of equal oppor­tun­it­ies.

We are cer­ti­fied as a fam­ily-friendly uni­versity. The Tech­nis­che Uni­versität Ber­lin and the Fraunhofer-Gesell­schaft pur­sue a fam­ily-friendly per­son­nel policy and offer their employ­ees flex­ible work­ing hours and sup­port to recon­cile work and fam­ily life. In addi­tion, the Dual Career Ser­vice of the Tech­nical Uni­versity of Ber­lin provides act­ive assist­ance to newly-appoin­ted people mov­ing with their entire fam­ily. Applic­a­tions from abroad are expli­citly wel­come.

Please send your applic­a­tion until May 8, 2020 indic­at­ing the ref­er­ence num­ber and includ­ing the appro­pri­ate doc­u­ment­a­tion (includ­ing a CV list­ing pub­lic­a­tions, teach­ing exper­i­ence etc., cop­ies of aca­demic degrees, teach­ing port­fo­lio and draft of pro­spect­ive teach­ing and research pro­jects, as well as cop­ies of up to five selec­ted pub­lic­a­tions) only in digital format by e-mail to berufungen@eecs.tu-berlin.de : Tech­nis­che Uni­versität Ber­lin – Der Präsid­ent – Dekan der Fak­ultät IV, Sekr. MAR 6-1, March­str. 23, 10587 Ber­lin.
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Facts

ID: 79183

Num. of em­ploy­ees: ca. 8300
Site: Charlottenburg, Berlin (Berlin, Germany)
Type: professorship
Cat­egory (TU Ber­lin): Pro­fessor
Dur­a­tion: lim­ited to 5 years
Part-/Full-time: full-time
Start­ing date (earli­est): At the earli­est poss­ible
Remu­n­er­a­tion: Salary grade W2
Scope: machine learning and communication,research,com­puter sci­ences,teach­ing
Field of stud­ies: machine learning, com­puter sci­ence
Level of edu­c­a­tion: Fulfilment of the requirements for appointment according to § 100 Berlin Higher Education Act.
Lan­guage skills:
German (excellent knowledge of language)
English (good knowledge of language)
Job offer is addi­tion­ally pub­lished at:
academics.com


Make an Ap­plic­a­tion

Clos­ing date:08/05/20
Re­ference num­ber:IV-238/20
Con­tact per­son:Anita Hummel
Con­tact phone:+49 (0)30 314-73260
By mail:
Technische Universität Berlin
- Der Präsident -
Dekan der Fakultät IV, Prof. Dr. Rolf Niedermeier, Sekr. MAR 6-1, Marchstr. 23, 10587 Berlin
By e-mail:berufungen@eecs.tu-berlin.de

Applic­a­tion papers:

including a CV listing publications, teaching experience etc., copies of academic degrees, teaching portfolio and draft of prospective teaching and research projects, as well as copies of up to five selected publications

Apply