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Òâ´óÀûÃ×À¼Àí¹¤´óѧDaniele Ielmini¿ÎÌâ×éÕÐResearch Fellow/Postdoctor

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Daniele Ielmini½ÌÊÚÊÇÐÂÐÍ´æ´¢Æ÷ºÍ´æÄÚ¼ÆËãÁìÓòµÄ¹ú¼ÊÖªÃûר¼Ò£¬¹ýÈ¥Ò»ÄêÔÚPNAS/Nat. Electron./Nat. Commun./Sci. Adv./Adv. Mater./IEDMµÈ¹ú¼Ê¶¥¼¶ÆÚ¿¯¡¢»áÒé¾ùÓÐÂÛÎÄ·¢±í£¬²¢ÓÚÈ¥Ä굱ѡIEEE Fellow¡£ÂÛÎÄ·¢±í×ÜÊý>300£¬¹È¸èÒýÓÃÂÊ>12000.

Õâ´ÎÕÐƸÖ÷ÒªÊÇÏë¼ÓËÙÒ»¸ö´´ÒµÏîÄ¿µÄÉÌÒµ»¯½ø¶È¡£×÷Ϊ¶ÔÕâ¸öÏîÄ¿µÄ³õ²½Á˽⣬¿ÉÒԲο´ÎÒÃÇ×î½ü·¢±íµÄÎÄÕ£¨https://www.pnas.org/content/early/2019/02/15/1815682116£©¡£ÎÄÕÂÄÚÈÝÒÑÉêÇëרÀû¡£¸Ã´´ÒµÏîÄ¿È¥Äê»ñµÃÁËÓÉѧУºÍµÂÇÚ¾Ù°ìµÄ²úƷת»¯´óÈüµÄ¹Ú¾ü£¬Ä¿Ç°£¬ÔÚѧУ·õ»¯Æ÷ºÍµÂÇÚµÈ×Éѯ¹«Ë¾µÄ°ïÖúÏ£¬ÎÒÃÇ×¼±¸°Ñ¼¼Êõ´øÏòÊг¡¡£´´ÒµÍŶÓÓÉDaniele Ielmini½ÌÊÚ¡¢ËïÖÙ²©Ê¿ºÍһλÒâ´óÀû²©Ê¿Éú×é³É£¬ÏîÄ¿ÄÚÈÝÈçÏ£º

Learning from big data is the next big thing for a wide range of fields, including communications, industry,
business, and healthcare. The von Neumann computer architecture is however not efficient for these tasks,
due to the large energy and time needed for data movement between the memory and the CPU/GPU. In
the frame of an ERC project, we are developing in-memory computing accelerators for machine learning
and data analytics, to process information within analog memory circuits. Our technology enables one-shot
learning with a 10,000x reduction of energy and time thanks to non-iterative computing in cross-point
arrays of resistive memories (memristors).

This project aims at demonstrating this performance in hardware by (i) designing integrated circuits with
foundry design kit including resistive memory, (ii) testing the hardware circuits fabricated by the industrial
partner foundry. The researcher should interact with a team of about 15 PhDs/postdocs
working on in-memory computing using resistive switching memory (RRAM) and phase change memory
(PCM).

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Your tasks:
To design integrated in-memory accelerator circuits
To design the testing board and test the circuit prototypes for practical data problems
To actively interact with the team members for device and theory development
To become actively involved in the dissemination/communication activities within the project
To mentor junior staff

Education:
PhD (or Master if adequate) in electrical engineering

Essential Knowledge and Professional Experience:
Proficiency in modern integrated circuit design using Cadence and Mentor Graphics tools
Fluent English, both spoken and written

Salary:
33k€ yearly gross salary (2k€ monthly net rate)

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Daniele Ielmini½ÌÊÚµÄÓÊÏ䣺daniele.ielmini@polimi.it£»ÎÒµÄÓÊÏ䣺zhong.sun@polimi.it
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