{"id":2703517,"url":"https://alion.io/job/axis-communications-master-thesis-approximative-computing-for-cnns","title":"Master Thesis - Approximative Computing for CNNs","company":{"id":4200,"name":"Axis Communications","domain":"axis.com","url":"https://alion.io/company/axis-communications","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":93,"open_postings":14,"ghost_share":0,"stale_share":0.286,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Hardware","role_family":"Hardware","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lund, Sweden"],"countries":["SE"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":56000,"max_usd":164000,"period":"year","method":null,"sample_n":3689},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Edge AI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Intercom","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"PyTorch C++","optional":false},{"name":"Quantization","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false},{"name":"Verilog","optional":false},{"name":"VHDL","optional":false}],"status":"live","first_seen_at":"2026-10-15T00:00:00Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-16T16:57:54Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Job Title\nMaster Thesis - Approximative Computing for CNNsJob Description\nCategory\nComputer Science, Machine Learning, Electronics, Integrated Circuits\nScope\nTwo Master students completing 30 credits each\nBackground\nConvolutional neural networks for object detection and semantic segmentation are increasingly deployed directly on edge devices, where power consumption and silicon area are tightly constrained. Most of the energy and area in a neural network accelerator is spent on multiply-accumulate operations. Approximate computing offers a way to reduce these costs by replacing exact arithmetic circuits with simpler alternatives that trade numerical accuracy for lower power consumption and smaller circuit area. Because neural networks are inherently tolerant to numerical noise, approximate multipliers have shown promising results for image classification. However, their impact on dense-prediction tasks such as object detection and semantic segmentation, where precise localization and per-pixel accuracy matter, remains largely unexplored.\nGoal\nThis thesis will investigate how approximate arithmetic circuits affect the accuracy and hardware efficiency of CNNs for object detection and semantic segmentation. Using established open-source libraries of approximate multipliers and simulation frameworks, you will:\nEvaluate how approximate multipliers affect detection and segmentation performance. \nQuantify the trade-off between network accuracy and estimated power and area savings. \nExplore methods for recovering lost accuracy, such as layer-wise selection of multipliers, calibration, and fine-tuning. \nWho are you?\nWe're looking for two Master's students in Electrical Engineering, Computer engineering, Computer science, or a related field who enjoy working where hardware meets machine learning. You're curious about how algorithms map onto silicon and motivated by turning experimental results into clear, well-founded conclusions.\nYou should have:\nSolid programming skills in Python, and experience with a deep learning framework such as PyTorch or Tensorflow \nA basic understanding of convolutional neural networks and how they are trained and evaluated \nCoursework or experience in digital design or computer architecture, including computer arithmetic \nIt's a plus if you have:\nExperience with object detection or semantic segmentation models \nFamiliarity with neural network quantization or hardware-aware model optimization \nExperience with hardware description languages (Verilog/VHDL) or synthesis tools \nKnowledge of C/C++ and/or CUDA \nAn interest in energy-efficient computing and edge AI \nIf you're applying as a pair, we especially welcome complementary profiles, for example one person with a stronger machine learning background and one with a stronger hardware background.\nOK, I am interested! What do I do now?\nYou are valuable to us - how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.\nApplications are accepted in both Swedish and English, and you apply via the proposal advert. \nThe announced thesis is open only to students affiliated with a Swedish University/College either directly or via an exchange program. \nWhen the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us. \nPlease attach your CV and University/College grade summary. \nWho to contact for any questions regarding the thesis assignment!\nTiger Moberg, \nType of Employment\nThesis Worker (Fixed Term)Posting End Date\n2026-10-14Certain roles at Axis require background checks, which means applicable verifications will be done in these recruitments. Notice will be provided before we take any action.\nAbout Axis Communications\nWe enable a smarter and safer world by creating innovative solutions for improving security and business performance. As a network technology company and industry leader, we offer solutions in video surveillance, access control, intercom, and audio systems, enhanced by intelligent analytics applications.\nWith around 6,000 committed employees in over 50 countries, we collaborate with partners worldwide. Together, we thrive in our friendly, open, and collaborative culture and inspire each other to think beyond the expected. United by our commitment to inclusion, diversity, and sustainability, we consistently seek to develop our skills and way of working.\nLet´s create a smarter and safer world\nFor more information about Axis, please visit our website www.axis.com.\nListen to Axis Communications Playlist","description_format":"text","description_chars":4787,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Video Surveillance","Access Control & Alarm Systems"],"lifecycle":[{"event":"open","at":"2026-10-16T11:54:52Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":2,"expected_fill_days":28,"reasons":["conf:12","win:early","comp:brand"],"computed_at":"2026-10-17T05:25:15Z"},"pay":null,"html_url":"https://alion.io/job/axis-communications-master-thesis-approximative-computing-for-cnns","json_url":"https://alion.io/job/axis-communications-master-thesis-approximative-computing-for-cnns.json","meta":{"generated_at":"2026-10-17T05:25:15Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":7313,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":4200},"rest":"https://alion.io/mcp/rest/get_company?id=4200"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Faxis-communications-master-thesis-approximative-computing-for-cnns"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Faxis-communications-master-thesis-approximative-computing-for-cnns"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Faxis-communications-master-thesis-approximative-computing-for-cnns"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/axis-communications-master-thesis-approximative-computing-for-cnns\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Faxis-communications-master-thesis-approximative-computing-for-cnns"}]}