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Tech­ni­sche Uni­ver­si­tät Dres­den - Cen­ter for Advan­cing Elec­tro­nics Dres­den

The TU Dres­den is one of ele­ven Ger­man uni­ver­si­ties that were iden­ti­fied as an “excel­lence uni­ver­sity”. TUD has about 36.500 stu­dents and almost 5319 employees, 507 pro­fes­sors among them, and, thus, is the lar­gest uni­ver­sity in Sax­ony, today.

Having been com­mit­ted to sci­en­ces and the engi­nee­ring before the reuni­fi­ca­tion of Ger­many, TU Dres­den now is a multi-disci­pline uni­ver­sity, also offe­ring huma­nities and social sci­en­ces as well as medi­cine.

Post­doc in Sto­chastic and Non­linear Dyna­mics

The newly estab­lished Chair of Net­work Dyna­mics hea­ded by Prof. Marc Timme and the Junior Rese­arch Group “Bio­lo­gi­cal Algo­rithms” hea­ded by PD Dr. Ben­ja­min Fried­rich, both affi­lia­ted with the ‘Cen­ter for Advan­cing Elec­tro­nics Dres­den’ (cfaed) and the Clus­ter of Excel­lence ‘Phy­sics of Life’ (PoL), jointly offer, sub­ject to resour­ces being avail­able, a
Post­doc in Sto­chastic and Non­linear Dyna­mics
(sub­ject to per­so­nal qua­li­fi­ca­tion employees are remu­ne­ra­ted accord­ing to salary group E 13 TV-L)
Rese­arch area: Pre­dic­ta­ble collec­tive dyna­mics of bio-inspi­red reser­voir net­works
Terms: The posi­tion is avail­able from 15th April, 2020 until 30th Sept, 2022. The period of employ­ment is gover­ned by the Fixed-Term Rese­arch Con-tracts Act (Wis­sen­schafts­zeit­ver­trags­ge­setz – WissZeitVG).

The Chair of Net­work Dyna­mics and the Junior Rese­arch Group Bio­lo­gi­cal Algo­rithms strive for a con­cep­tual under­stan­ding of the princi­ples under­ly­ing self-orga­ni­zed collec­tive dyna­mics, infor­ma­tion pro­ces­sing and con­trol in com­plex sys­tems, brid­ging phy­sics and bio­logy to com­pu­ter sci­ence and engi­nee­ring, see http://networkdynamics.info and https://cfaed.tu-dresden.de/friedrich-home.

About the pro­ject
How arti­fi­cial neural net­works pro­cess and share infor­ma­tion, is still poorly unders­tood. The con­se­quence is the dif­fi­cult and often very limi­ted pre­dic­ta­bi­lity of lear­ning out­co­mes and thus a high level of non-trans­pa­rency. Wit­hin the pro­ject Trans­par­Net, you will pioneer theo­re­ti­cal approa­ches to improve pre­dic­ta­bi­lity and thus trans­pa­rency of Arti­fi­cial Intel­li­gence metho­do­logy for net­wor­ked com­pu­ting by exploi­t­ing the para­digm of reser­voir com­pu­ting, where a pre-pro­ces­sing unit with fixed weights (the reser­voir) is cou­pled to an out­put layer.

Work­ing field:

The suc­cess­ful can­di­date will deve­lop and apply methods from the non­linear dyna­mics of com­plex sys­tems, from net­work dyna­mics and from sta­tis­ti­cal phy­sics to deve­lop a pre­dic­tive theory of reser­voir dyna­mics. Under­stan­ding cor­re­la­ted non­linear dyna­mics, mul­tista­bi­lity, and infor­ma­tion rou­ting in multi-dimen­sio­nal net­works will pave the road towards effi­ci­ent and trans­pa­rent lear­ning rules for the out­put layer, espe­ci­ally for the pro­ces­sing of time-con­ti­nuous and dyna­mic input signals, a fea­ture cur­r­ently out of reach for con­ven­tio­nal arti­fi­cial neu­ro­nal net­works.

Require­ments:

We are loo­king for a theo­re­ti­cal phy­si­cist (or app­lied mathe­ma­ti­cian) who is moti­va­ted to per­form cut­ting-edge rese­arch at the inter­face of phy­sics and infor­ma­tion sci­ence, and meets the fol­lo­wing requi­re­ments:
  • out­stan­ding uni­ver­sity doc­to­ral degree (PhD) in Theo­re­ti­cal Phy­sics, or clo­sely rela­ted field
  • expe­ri­ence in sta­tis­ti­cal phy­sics, non­linear dyna­mics, sto­chastic pro­ces­ses; infor­ma­tion theory is a plus
  • expe­ri­ence in Com­pu­ta­tio­nal Phy­sics (ODEs, PDEs, SDEs), and strong pro­gramming skills (e.g. Julia, Mat­lab, Python, C++)
  • strong inte­rest in app­ly­ing phy­sics to under­stand bio­lo­gi­cal and bio-inspi­red pro­ces­ses, and the wil­ling­ness to learn some bio­logy en route
  • strong ana­ly­tic and pro­blem-sol­ving skills, crea­ti­vity,
  • an inde­pen­dent, tar­get- and solu­tion-dri­ven work atti­tude,
  • strong com­mu­ni­ca­tion skills, espe­ci­ally in cross-disci­pli­nary com­mu­ni­ca­tion
  • flu­ency in Eng­lish – oral and writ­ten; Ger­man lan­guage skills are a plus but not initi­ally requi­red.

What we of­fer:

You will join an enthu­si­astic and ambi­tious rese­arch group, where you can drive your pro­ject for­ward and bene­fit from inspi­ra­tio­nal inter­ac­tions with like-min­ded rese­ar­chers. On this pro­ject, you will clo­sely inter­act with a PhD stu­dent working on the same pro­ject, giving you the oppor­tu­nity to gain (first) expe­ri­ence in super­vi­sing stu­dents by super­vi­sing the stu­dent jointly. The working lan­guage of our two inter­na­tio­nal teams is Eng­lish.

Dres­den is a European hub for Infor­ma­tion Sci­en­ces, Bio­lo­gi­cal Phy­sics and Engi­nee­ring. You will be embed­ded in two rese­arch clus­ters, where we con­tri­bute insights into collec­tive non­linear net­work dyna­mics, bio-inspi­red algo­rithms and bio­lo­gi­cal infor­ma­tion pro­ces­sing. As part of the Clus­ter of Excel­lence “Phy­sics of Life” and the Cen­ter for Advan­cing Elec­tro­nics (cfaed), we enjoy the close pro­xi­mity of col­la­bo­ra­tion part­ners at the Max-Planck Insti­tu­tes for the Phy­sics of Com­plex Sys­tems, the Max Planck Insti­tute for Cell Bio­logy and Gene­tics, the Bio­tech­no­logy Centre, and the new Cen­ter for Sys­tems Bio­logy Dres­den.

For infor­mal enqui­ries, please con­tact PD Dr. Ben­ja­min Fried­rich at benjamin.m.friedrich@tu-dresden.de.

App­li­ca­ti­ons from women are par­ti­cu­larly wel­come. The same app­lies to people with disa­bi­li­ties.

About TU Dres­den
The TU Dres­den is among the top uni­ver­si­ties in Ger­many and Europe and one of the ele­ven Ger­man uni­ver­si­ties that were iden­ti­fied as an ‘elite uni­ver­sity’. As a modern full-sta­tus uni­ver­sity with 18 depart­ments it offers a wide aca­de­mic range making it one of a very few in Ger­many.

How to ap­ply:

Your app­li­ca­tion (in Eng­lish only) should include: a moti­va­tion let­ter, your CV with publi­ca­tion list, the names and con­tact details of two refe­ren­ces, copy of degree cer­ti­fi­cate, and tran­script of gra­des (i.e. the offi­cial list of cour­se­work inclu­ding your gra­des). Please include also a link to your Mas­ter’s or PhD the­sis. Com­plete app­li­ca­ti­ons should be sub­mit­ted pre­fer­a­bly via the TU Dres­den Secu­re­Mail Por­tal https://securemail.tu-dresden.de by sen­ding it as a sin­gle pdf docu­ment quo­ting the refe­rence num­ber PD-Bio0120 in the sub­ject hea­der to recruiting.cfaed@tu-dresden.de or alter­na­tively by post to: TU Dres­den, cfaed, Herrn PD Dr. Ben­ja­min Fried­rich, Helm­holtz­str. 10, 01069 Dres­den, Ger­many. The clo­sing date for app­li­ca­ti­ons is 13.02.2020 (stam­ped arri­val date of the uni­ver­sity cen­tral mail ser­vice app­lies). Please sub­mit copies only, as your app­li­ca­tion will not be retur­ned to you. Expen­ses incur­red in atten­ding inter­views can­not be reim­bur­sed.


Refe­rence to data pro­tec­tion: Your data pro­tec­tion rights, the pur­pose for which your data will be pro­ces­sed, as well as fur­ther infor­ma­tion about data pro­tec­tion is avail­able to you on the web­site: https://tu-dresden.de/karriere/datenschutzhinweis