Python Scipy Jobs

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Hourly - Intermediate ($$) - Est. Time: 1 to 3 months, 30+ hrs/week - Posted
RESPONSIBILITIES Implement newest deep learning algorithms from research literature Develop algorithms with our senior researchers Brainstorm new algorithmic ideas Read new articles and introduce them to team Analyze performance of used algorithms Contribute to the technical discussions within the team and contribute new ideas. Gather materials for dialogue + deep learning blog / newsletter MUST HAVE Degree or experience in computer science research or software development Basic understanding of machine learning and deep learning Good programming skills Good English communication skills Ability to work in a team, good organisational skills and a positive attitude Passion to improve dialogue modeling using artificial intelligence NICE TO HAVE Academic or industry experience in deep learning. Familiarity with existing deep learning libraries (e.g. TensorFlow, Torch, Theano, Caffe, PyBrain, etc.). Familiarity with SciPy stack. Ability to code in C/C++ and Python. Professional experience in software development. Comfortable working in Linux environment.
Skills: Python SciPy C++ Deep Learning Machine learning
Hourly - Expert ($$$) - Est. Time: 1 to 3 months, 30+ hrs/week - Posted
We need a Deep Learning Research Engineer who is passionate about taking AI to the next level, and who is interested in building the company alongside the founders. YOUR RESPONSIBILITIES Develop new methodologies for dialogue modeling, leveraging latest research in deep learning, machine learning, natural language processing and question answering. Prototype integration of developed algorithms into a final product. Develop, implement and test deep learning research ideas together with the team. Contribute to the technical discussions within the team and contribute new ideas. MUST HAVE Master’s degree, PhD or equivalent in artificial intelligence, deep learning, machine learning, data science, computer science, or a similar field. Academic or industry experience in deep learning. Familiarity with existing deep learning libraries (e.g. TensorFlow, Torch, Theano, Caffe, PyBrain, etc.). Passion to develop and expand deep learning technologies. Strong programming skills. Good English communication skills. Ability to work in a team, good organisational skills and a positive attitude. Passion to improve dialogue modeling using artificial intelligence. NICE TO HAVE Experience in implementing algorithms in TensorFlow. Familiarity with SciPy stack. Professional experience in software development. Ability to code in C/C++ and Python. CUDA optimization experience. Understanding of reinforcement learning. Strong publication record in the relevant venues such as ICML, NIPS, ICLR, ACL, ECML or KDD. Comfortable working in Linux environment. Familiarity with agile software development.
Skills: Python SciPy C++ Deep Learning Machine learning
Hourly - Expert ($$$) - Est. Time: 3 to 6 months, 10-30 hrs/week - Posted
We are data science consulting company based in California www.einsteinassembly.com We are looking for 4 freelance data scientist for our consulting clients If you have good knowledge of R or Python and specialized in any of Machine learning area with business domains like Real Estate , Marketing, Sports, Social Media, web analytics, NLP , Recommendation models . Reinforcement Learning, Expert Systems in AI. Please apply with sample project sharing. The selection will be based on you knowledge and experience in specific projects.
Skills: Python SciPy Algorithms Apache Spark Artificial Intelligence
Hourly - Expert ($$$) - Est. Time: 3 to 6 months, 10-30 hrs/week - Posted
We have written some machine learning scripts in Python that are now breaking on several datasets. We need some help with someone that understands coding in python.
Skills: Python SciPy Pandas Python Numpy
Fixed-Price - Expert ($$$) - Est. Budget: $9,500 - Posted
We are looking for a highly experienced developer with advanced design skills or access to a highly advanced design team. We are requesting proposals for a Blockchain as a service Platform that functions similarly to IBM's Blockchain Labs on Bluemix. If you are unfamiliar with it you can create a free test account on Bluemix and follow the instructions to access the Blockchain Labs Devops section. Essentially what it is, is taking open source Blockchain code from github and running it on a cloud instance so it can be spun up at the click of a button. User creates VM -> triggers node to deploy -> blockchain can then be customizable and rest API exposed. More documentation is available by looking at IBM's docs about their Blockchain as a service platform. We are using that as an example because it is similar to what we want developed. Please submit your qualifications, examples of your work, and what questions you have.
Skills: Python SciPy AngularJS API Development CSS3
Hourly - Expert ($$$) - Est. Time: More than 6 months, Less than 10 hrs/week - Posted
Mid-level developer to help build and grow back end infrastructure. Need someone who is eager and excited by the idea of building systems to scale. Someone who wants to bring new technology and creativity to solving back end infrastructure issues or do Application Development in Python. Someone who wants to participate in building a market leading data analytics platform. 2-5 years of experience in Python (preferably) or comparable language BS or MS degree (Sciences, Technology, Engieneering) Knowledge of an RDBS Experience in a Linux environment Professional experience scaling software projects in an enterprise environment Extensive knowledge of Python libraries (SciPy, Numpy, etc)
Skills: Python SciPy Python Python Numpy
Fixed-Price - Entry Level ($) - Est. Budget: $70 - Posted
I have several mql4 EAs that I would like to convert to Python. This is the first of many projects. The python code must work on Quantopian and you can read about it here: https://www.quantopian.com/tutorials/getting-started see below an example of python code on Quantopian: see below an example of python code on Quantopian: def initialize(context): """ initialize() is called once at the start of the program. Any one-time startup logic goes here. """ # An assortment of securities from different sectors: # MSFT, UNH, CTAS, JNS, COG context.security_list = [sid(5061), sid(7792), sid(1941), sid(24556), sid(1746)] # Rebalance every Monday (or the first trading day if it's a holiday) # at market open. schedule_function(rebalance, date_rules.week_start(days_offset=0), time_rules.market_open()) # Record variables at the end of each day. schedule_function(record_vars, date_rules.every_day(), time_rules.market_close()) def compute_weights(context, data): """ Compute weights for each security that we want to order. """ # Get the 30-day price history for each security in our list. hist = data.history(context.security_list, 'price', 30, '1d') # Create 10-day and 30-day trailing windows. prices_10 = hist[-10:] prices_30 = hist # 10-day and 30-day simple moving average (SMA) sma_10 = prices_10.mean() sma_30 = prices_30.mean() # Weights are based on the relative difference between the short and long SMAs raw_weights = (sma_30 - sma_10) / sma_30 # Normalize our weights normalized_weights = raw_weights / raw_weights.abs().sum() # Determine and log our long and short positions. short_secs = normalized_weights.index[normalized_weights < 0] long_secs = normalized_weights.index[normalized_weights > 0] log.info("This week's longs: " + ", ".join([long_.symbol for long_ in long_secs])) log.info("This week's shorts: " + ", ".join([short_.symbol for short_ in short_secs])) # Return our normalized weights. These will be used when placing orders later. return normalized_weights def rebalance(context, data): """ This function is called according to our schedule_function settings and calls order_target_percent() on every security in weights. """ # Calculate our target weights. weights = compute_weights(context, data) # Place orders for each of our securities. for security in context.security_list: if data.can_trade(security): order_target_percent(security, weights[security]) def record_vars(context, data): """ This function is called at the end of each day and plots our leverage as well as the number of long and short positions we are holding. """ # Check how many long and short positions we have. longs = shorts = 0 for position in context.portfolio.positions.itervalues(): if position.amount > 0: longs += 1 elif position.amount < 0: shorts += 1 # Record our variables. record(leverage=context.account.leverage, long_count=longs, short_count=shorts)
Skills: Python SciPy MetaTrader 4 (MT4) MQL 4 Python
Fixed-Price - Entry Level ($) - Est. Budget: $55 - Posted
Am looking for someone with knowledge in Python and it's surrounding skills. I have a project that will need help over the next few months and will need someone familiar with Python, Pandas, MySQL, MySQlite, Django if possible. There will be many simple small jobs and tasks to complete over the next few months. If you think you are the appropriate candidate, please add me on skype where we can go into more detail, skype name: strictly.vegas DO NOT add me if you haven't used python before
Skills: Python SciPy Django Pandas Python