haikurion

Basis Research Institute | Research Scientist/Engineer, Ops, Interns | In-person + Hybrid | http://basis.ai

Basis is a new nonprofit research institute. We're building a foundation of (approximately) universal reasoning-based AI, to help solve hard scientific and societal problems in collaboration with domain experts.

Research scientists/engineers, get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Probabilistic ML

- Bayesian / Causal inference

- Program synthesis & analysis

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

Operations people get in touch if you're interesting in building a new kind of organization from the ground up.

Apply to roles at https://www.basis.ai/join-us/ and/or write to hiring@basis.ai for more info.

Basis Research Institute | Research Scientist/Engineer, Operations Manager, Interns | In-person + Remote | http://basis.ai

Basis is a new nonprofit research institute. We're building a foundation of (approximately) universal reasoning-based AI, to help solve hard scientific and societal problems in collaboration with domain experts.

Research scientists/engineers, get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Probabilistic ML

- Bayesian / Causal inference

- Program synthesis & analysis

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

Operations people get in touch if you're interesting in building a new kind of organization from the ground up.

Apply to roles at https://www.basis.ai/join-us/ and/or write to hiring@basis.ai for more info.

Columbia University - Data Science Institute | Research Software Engineer | In-person + Remote

We're looking for someone eager to work at the cutting edge of research, helping to develop the next generation of causal and probabilistic programming languages.

Get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Probabilistic ML

- Bayesian / Causal inference

- Program synthesis & analysis

You’ll work with me (http://www.zenna.org/) and the DSI, doing:

- Language design & implementation

- Optimizing / scaling research code

- Algorithm development

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

To apply, please write to zt2297@columbia.edu

Columbia University - Data Science Institute | Research Software Engineer | In-person + Remote

We're looking for someone eager to work at the cutting edge of research, helping to develop the next generation of causal and probabilistic programming languages.

Get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Probabilistic ML

- Bayesian / Causal inference

- Program synthesis & analysis

You’ll work with me (http://www.zenna.org/) and the DSI, doing:

- Language design & implementation

- Optimizing / scaling research code

- Algorithm development

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

To apply, please write to zt2297@columbia.edu

Columbia University - Data Science Institute | Research Software Engineer | In-person + Remote

We're looking for someone eager to work at the cutting edge of research, helping to develop the next generation of causal and probabilistic programming languages.

Get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Probabilistic ML

- Bayesian / Causal inference

- Program synthesis & analysis

You’ll work with me (http://www.zenna.org/) and the DSI, doing:

- Language design & implementation

- Optimizing / scaling research code

- Algorithm development

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

To apply, please write to zt2297@columbia.edu

If I didn’t know better I would think this is a parody of morally bankrupt technologists. But it’s not a parody, it’s an obscene reality.

- They have constructed a (rather impressive) mass deception machine, and put it to work.

- They have convinced themselves that the mass deception is worthwhile because their clients can’t lose money.

- When challenged, they seem to believe that the ends justifies the means, and that it is just marketing.

The only constructive thing I can think of is that those of us who have the ability to build these kinds of things should take this as another case study of what not to do, and how not to respond to criticism.

Columbia University - Data Science Institute | Research Software Engineer | In-person + Remote

We're looking for someone eager to work at the cutting edge of research, helping to develop the next generation of causal and probabilistic programming languages.

Get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Bayesian / Causal inference

- Machine learning

- Program synthesis & analysis

You’ll work with me (http://www.zenna.org/) and the DSI, doing:

- Language design & implementation

- Optimizing / scaling research code

- Algorithm development

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

To apply, please write to zt2297@columbia.edu

Columbia University - Data Science Institute | (Research) Software Engineer | Remote

We're looking for someone eager to work at the cutting edge of research, helping to develop the next generation of causal and probabilistic programming languages.

Get in touch if you're passionate about any of:

- Compilation / program transformations

- Probabilistic programming

- Bayesian / Causal inference

- Machine learning

- Program synthesis & analysis

You’ll work with me (http://www.zenna.org/) and the DSI, doing:

- Language design & implementation

- Optimizing / scaling research code

- Algorithm development

Ideal attributes:

- Strong programmer. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

To apply, please write to zt2297@columbia.edu

Shameless plug for a new compiler-related position at Columbia, working with me - https://news.ycombinator.com/item?id=27704543

Columbia University - Data Science Institute | (Research) Software Engineer | Full/Part-time | Remote/Onsite

We are looking for a research software engineer eager to work in an academic environment at the cutting edge of probabilistic programming, causal inference, program synthesis and machine learning.

Role:

You will play an integral part in developing systems for automatic causal and probabilistic inference. Our goal is to build systems that can reason coherently about the real world, in all of its complexity and ambiguity. These systems should allow people to (semi-automatically) build sophisticated models of the world, determine causal effects, design experiments, and construct explanations. An immediate application is in algorithmic fairness.

Relevant areas:

- Probabilistic programming

- Causal inference - Machine learning

- Program synthesis

- Program analysis

- Automated theorem proving

You’ll work with me (http://www.zenna.org/) and the DSI community.

Responsibilities:

- Programming language design/implementation

- Performance engineering, scaling research code

- Algorithm development

- Application to real-world problems

Preferred Qualifications:

- Strong coding ability. esp. Julia, Python, C++, ML-family

- Comfortable digesting research e.g. from PLDI, POPL, NeurIPS or ICML

- Good software engineering practices

- Able to progress with high degree of autonomy, and under uncertainty

To apply, please write to zt2297@columbia.edu

Research Engineer / Technical Writers | Full time or part time | Columbia University

I’m starting a research team at Columbia University. Please get in touch if you are interested in working in an academic environment at the cutting edge of probabilistic programming, causal inference, program synthesis and machine learning.

All levels of experience will be considered. Respondents should have familiarity with some subset of the following:

- Bayesian inference

- Probabilistic programming

- Structural causal modeling

- Machine learning

- The Julia programming language

- Model based and model free planning and reinforcement learning

- Program synthesis

- Technical writing

- Grant writing

- Automated / interactive theorem proving

- Compilers and program analysis

Contact: zenna[at]csail[dot]mit[dot]edu

You can see the kind of research I do on my website zenna.org

Research Engineer | Full time or part time | NY University

I’ll be starting a research team within a NY University. Please get in touch if you are interested in working in an academic environment at the cutting edge of probabilistic programming, causal inference, program synthesis and machine learning.

All levels of experience will be considered. Respondents should have familiarity with some subset of the following:

- Bayesian inference

- Probabilistic programming

- Structural causal modeling

- The Julia programming language

- Model based and model free planning and reinforcement learning

- Program synthesis

- Technical writing

- Compilers and program analysis

Contact: zennatavares[at]gmail[dot]com

This position will start early next year. You can see the kind of research I do on my website zenna.org

Basis | Remote (out of MIT) | Research Engineer | Contract (Maybe Full Time)

This is a project on commercializing an MIT research project, synthesizing probabilistic programming, databases, and spreadsheets.

Desired skills - Languages: Julia, C++, Stan, others, react, elm - Programming language design: compilers, formal emthods, etc - Probabilistic programming - Probabilistic inference methods: MCMC, variational inference, HMC - Causal inference - Statistics - Numerical simulation - Databases: usage and design - Machine learning: deep learning, Bayesian deep learning - Design

zennatavares [at] gmail [dot] com

I think the execution of native code from the browser is slowly creeping in, in such a way that you'll be free to choose your language as you are in non-browser areas. Google native client, webGL and the ultra-new webCL are glimpses into the future. Javascript has served us well, but if we can avoid unnecessary computation as well as make use of existing code bases, then I am sure we will, eventually.

There are many flaws here. While a digital (or discrete) space (or realm of infinite possibilities as you put it) has a limited size as a function of the allowed length,

1. The length can be infinitely long, therefore the number of possibilities can be infinitely long.

2. For all practical purposes the space is so large it is infinite. How many possible permutations of 1s and 0s can you get in a 1024 KB program. Many.

3. If we go into the more practical example you raised I doubt there is much difference between the variance in resulting table legs and digital storage applications. If anything, I would say there would be more variation in the software, simply because the high cost of iteration and experimentation (and stricter physical constraints) have narrowed the design space of table legs, in a way which is much less apparent in software.