Pro­fes­sor of Machine Learn­ing and Com­mu­nic­a­tion

Technical University Berlin

Germany

Tech­ni­sche Uni­ver­sität Ber­lin - Fac­ulty IV

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 29, 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.

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 TU web­site quick access 214041.
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Facts

ID: 80061

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:
29/05/20
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


In your application, please refer to Polytechnicpositions.com

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